CHRIST (Deemed to University), BangaloreDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERINGSchool of Engineering and Technology 

Syllabus for

3 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CS331P  DATABASE MANAGEMENT SYSTEMS  Core Courses  5  4  100 
CS332P  DATA STRUCTURES AND ALGORITHMS  Core Courses  5  4  100 
CS333  SOFTWARE ENGINEERING  Core Courses  3  3  100 
CSHO331AIP  STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE    4  4  100 
CSHO331CSP  PROBABILITY AND RANDOM PROCESS    4  4  100 
CSHO331DAP  STATISTICAL FOUNDATION FOR DATA ANALYTICS    4  4  100 
CY321  CYBER SECURITY  Ability Enhancement Compulsory Courses  2  0  0 
EC337  DIGITAL SYSTEMS  Core Courses  3  3  100 
HS311  TECHNICAL WRITING  Core Courses  2  2  50 
MA334  DISCRETE MATHEMATICS  Core Courses  3  3  100 
4 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
BS451  ENGINEERING BIOLOGY LABORATORY  Core Courses  2  2  50 
CS432P  OPERATING SYSTEMS  Core Courses  5  4  100 
CS433P  PROGRAMMING PARADIGM  Core Courses  5  4  100 
CS434  FORMAL LANGUAGE AND AUTOMATA THEORY  Core Courses  3  3  100 
CS435P  COMPUTER ORGANIZATION AND ARCHITECTURE  Core Courses  5  4  100 
CSHO432AIP  ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING    5  4  100 
CSHO432CSP  MOBILE AND NETWORK BASED ETHICAL HACKING    5  4  100 
CSHO432DAP  BIG DATA ANALYTICS    5  4  100 
EVS421  ENVIRONMENTAL SCIENCE  Add On Courses  2  0  0 
HS422  PROFESSIONAL ETHICS  Skill Enhancement Courses  2  2  50 
MA431  PROBABILITY AND QUEUING THEORY  Core Courses  3  3  100 
5 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CS531P  COMPUTER NETWORKS  Core Courses  5  4  100 
CS532  INTRODUCTION TO ARTIFICAL INTELLIGENCE  Core Courses  3  3  100 
CS533P  DESIGN AND ANALYSIS OF ALGORITHMS  Core Courses  5  4  100 
CS541E01  COMPUTER GRAPHICS WITH OPEN GL  Discipline Specific Elective Courses  3  3  100 
CS541E02  INTERNET AND WEB PROGRAMMING  Discipline Specific Elective Courses  3  3  100 
CS541E04  CRYPTOGRAPHY AND NETWORK SECURITY  Discipline Specific Elective Courses  3  3  100 
CS581  INTERNSHIP  I  Core Courses  2  1  50 
CSHO531AIP  STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE  Minors and Honours  5  4  100 
CSHO531CSP  PROBABILITY AND RANDOM PROCESS  Minors and Honours  5  4  100 
CSHO531DAP  STATISTICAL FOUNDATION FOR DATA ANALYTICS  Minors and Honours  5  4  50 
EC535OE01  EMBEDDED BOARDS FOR IOT APPLICATIONS  Generic Elective Courses  3  3  100 
EC535OE02  FUNDAMENTALS OF IMAGE PROCESSING  Generic Elective Courses  3  3  100 
EC535OE03  OBSERVING EARTH FROM SPACE  Generic Elective Courses  3  3  100 
EE536OE01  HYBRID ELECTRIC VEHICLES  Generic Elective Courses  4  3  100 
EE536OE02  ROBOTICS AND AUTOMATION  Generic Elective Courses  4  3  100 
EE536OE03  SMART GRIDS  Generic Elective Courses  3  3  100 
HS521  PROJECT MANAGEMENT AND FINANCE  Core Courses  3  3  100 
IC521  INDIAN CONSTITUTION  Ability Enhancement Compulsory Courses  2  0  50 
NCCOE01  NCC1  Generic Elective Courses  3  3  100 
6 Semester  2020  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
BTGE635  INTELLECTUAL PROPERTY RIGHTS  Generic Elective Courses  2  2  100 
BTGE636  INTRODUCTION TO AVIATION  Generic Elective Courses  2  2  100 
BTGE637  PROFESSIONAL PSYCHOLOGY  Generic Elective Courses  2  2  100 
BTGE651  DATA ANALYTICS THROUGH SPSS  Generic Elective Courses  2  2  100 
BTGE652  DIGITAL MARKETING  Generic Elective Courses  2  2  100 
BTGE653  DIGITAL WRITING  Generic Elective Courses  2  2  100 
BTGE654  PHOTOGRAPHY  Generic Elective Courses  2  2  100 
BTGE655  ACTING COURSE  Generic Elective Courses  2  2  100 
BTGE656  CREATIVITY AND INNOVATION  Generic Elective Courses  2  2  100 
BTGE657  PAINTING AND SKETCHING  Generic Elective Courses  2  2  100 
BTGE658  DESIGN THINKING  Generic Elective Courses  2  2  100 
CS631P  INTERNET OF THINGS  Core Courses  5  4  100 
CS632P  COMPILER DESIGN  Core Courses  5  4  100 
CS642E01  MOBILE APPLICATION DEVELOPMENT  Electives  3  3  100 
CS642E03  ADVANCED DATABASES  Electives  3  3  100 
CS681  SERVICE LEARNING  Skill Enhancement Courses  4  2  50 
CSHO631AIP  ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING    5  4  100 
CSHO631CSP  MOBILE AND NETWORK BASED ETHICAL HACKING    5  4  100 
CSHO631DAP  BIG DATA ANALYTICS    5  4  100 
CSHO632AIP  ROBOTICS AND PROCESS AUTOMATION    5  4  100 
CSHO632CSP  CYBER FORENSICS AND MALWARE DETECTION    5  4  100 
CSHO632DAP  BIG DATA SECURITY ANALYTICS    5  4  100 
IT633P  DATA WAREHOUSING AND DATA MINING  Core Courses  5  4  100 
IT642E02  FOUNDATIONS TO BLOCKCHAIN TECHNOLOGY  Electives  3  3  100 
7 Semester  2019  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CEOE761E01  SUSTAINABLE AND GREEN TECHNOLOGY  Core Courses  3  3  100 
CEOE761E03  GIS AND REMOTE SENSING TECHNIQUES AND APPLICATIONS  Core Courses  3  3  100 
CS743E01  UNIX SYSTEM PROGRAMMING  Core Courses  3  3  100 
CS743E02  TCP/IP DESIGN AND IMPLEMENTATION  Core Courses  3  3  100 
CS743E03  SIMULATION AND MODELING  Core Courses  3  3  100 
CS743E04  SOFTWARE PROCESS AND PROJECT MANAGEMENT  Core Courses  3  3  100 
CS744E01  INFORMATION STORAGE AND MANAGEMENT  Core Courses  3  3  100 
CS744E02  DATA BASE ADMINISTRATION  Core Courses  3  3  100 
CS744E03  NETWORK STORAGE TECHNOLOGIES  Core Courses  3  3  100 
CS744E04  NETWORK ADMINISTRATION  Core Courses  3  3  100 
CS744E05  RESEARCH METHODOLOGY  Core Courses  3  3  100 
CS781  INTERNSHIP  II  Core Courses  2  1  50 
CS782  SERVICE LEARNING  Core Courses  4  2  50 
CS783  PROJECT WORK PHASE  I  Core Courses  8  4  100 
IC721  CONSTITUTION OF INDIA  Skill Enhancement Courses  2  0  50 
MA736OE3  NUMERICAL SOLUTIONS OF DIFFERENTIAL EQUATIONS  Core Courses  3  3  100 
ME761E03  BASIC AUTOMOBILE ENGINEERING  Core Courses  3  3  100 
ME761E05  BASIC AEROSPACE ENGINEERING  Core Courses  3  3  100 
PH736OE1  NANO MATERIALS AND NANOTECHNOLOGY  Core Courses  3  3  100 
8 Semester  2019  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
CS845E01  QUANTUM COMPUTING  Core Courses  3  3  100 
CS845E02  MOBILE COMPUTING  Core Courses  3  3  100 
CS845E04  GRID COMPUTING  Core Courses  3  3  100 
CS846E01  COMPUTER AIDED DECISION SUPPORT SYSTEMS  Core Courses  3  3  100 
CS846E03  INTRODUCTION TO ROBOTICS  Core Courses  3  3  100 
CS846E04  HIGH PERFORMANCE COMPUTING  Core Courses  3  3  100 
CS846E07  NATURAL LANGUAGE PROCESSING  Core Courses  3  3  100 
CS881  PROJECT WORK  Core Courses  20  10  300 
CS882  COMPREHENSION  Skill Enhancement Courses  2  1  50 
 
Introduction to Program:  
The Undergraduate program in Information Technology is aimed at creating computer science engineers by providing the fundamentals of engineering and basic skills in computing. The special focus on employability is clear from the inclusion of subjects based on demand of industry and mandatory internships. A wellchosen elective basket gives the ward an opportunity to widen their knowledge in any specific domain.  
Programme Outcome/Programme Learning Goals/Programme Learning Outcome: PO1: Apply Engineering knowledge of computing, mathematics, science, and computer science & engineering fundamentals for Problem solving.PO2: Think critically to identify, formulate, and solve complex computer science & engineering problems by developing models, evaluating validity and accuracy of solutions in terms of computer science and engineering validity measures. PO3: Analyse, design of complex problems, implement, and evaluate a computerbased system, to meet expected needs with appropriate considerations such as economic / environmental/societal. PO4: Conduct experiments to investigate problems based on changing requirements, analyze and interpret results. PO5: Create, select, adapt appropriate techniques and use of the modern computational tools, techniques and skills, and best of engineering practices. PO6: Understand the impact of contextual knowledge on social aspects and cultural issues. PO7: Understand contemporary issues related to social & environmental context for sustainable development of engineering solutions. PO8: Understand professional & ethical responsibility to contribute for societal and national needs. PO9: Function and coordinate effectively as an individual, as a member or leader in diverse, multicultural& multidisciplinary teams PO10: Communicate effectively. PO11: Demonstrate an understanding of computer science and engineering & management principles to manage software projects. PO12: Demonstrate a recognition and realization of the need for, and an ability to engage in lifelong learning.  
Assesment Pattern  
II. ASSESSMENT  ONLY FOR THEORY COURSE (without practical component)
Components of the CIA CIA I: Subject Assignments / Online Tests: 10 marks CIA II: Mid Semester Examination (Theory): 25 marks CIAIII: Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications: 10 marks Attendance: 05 marks Total: 50 marks
Mid Semester Examination (MSE) : Theory Papers:
End Semester Examination (ESE): The ESE is conducted for 100 marks of 3 hours duration. The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution. Question paper pattern is as follows. Two full questions with either or choices will be outlined from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a particular question. The objective of the question paper is to test the application and analytical skill of the student. The major purpose of the question paper is to bring clarity about the process of associating questions to their respective performance indicators and hence to improve the ratings in course outcomes. Further, these question papers demonstrate how bloom’s taxonomy can be used to map the quality of question papers along with their effectiveness in the assessment pattern.
III. ASSESSMENT OF COMPREHENSION, INTERNSHIP and SERVICE LEARNING COMPREHENSION Maximum Marks = 50 Passing marks 40% min Do not have ESE and completely evaluated through continuous assessment only,
The evaluation (minimum 2 presentations) shall be based on the
INTERNSHIP
Maximum Marks = 50(Only credit will be displayed in the score card) Passing marks 40% min Do not have ESE and completely evaluated through continuous assessment only Continuous Internal Assessment is based upon
SERVICE LEARNING Maximum Marks = 50 Passing marks 40% min Do not have ESE and completely evaluated through continuous assessment only, Comprising
V. ASSESSMENT OF PROJECT WORK Project PhaseI Project work may be assigned to a single student (with due approval from department) or to a group of students not exceeding 4 per group. Maximum Marks = 100
ESE 100 MARKS IS EVALUATED AS
Project PhaseII Project work may be assigned to a single student (with due approval from department) or to a group of students not exceeding 4 per group. Maximum Marks = 300 ● Continuous Assessment: 200 marks. ● End Semester Examination (project report evaluation and vivavoce) : 100 marks. ● The continuous assessment and End Semester Examinations marks for Project Work and the VivaVoce Examination will be distributed as indicated below.
● There shall be 3 reviews and the student shall make presentation on the progress made before the committee constituted by the Department ● The total marks obtained in the 3 reviews shall be 200 marks.
ESE 100 MARKS are evaluated as ● Initial Write Up : 10marks ● Viva Voce : 25 marks ● Demonstration : 40 marks ● Project Report : 25 marks
Holistic Education: End Semester Examination :25 Marks Participation:25 Marks Total :50 Marks  
Examination And Assesments  
Continuous internal AssesmentCIAI,CIAII,CIAIII End Semester Examination 
CS331P  DATABASE MANAGEMENT SYSTEMS (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To learn the fundamentals of data models and to conceptualize and depict a database system using ER diagram. To make a study of SQL and relational database design. To understand the internal storage structures using different file and indexing techniques which will help in physical DB design. To know the fundamental concepts of transaction processing concurrency control techniques and recovery procedure. To have an introductory knowledge about the emerging trends in the area of distributed DBOO DB Data mining and Data Warehousing and XML. To implement the design of the tables in DBMS. To write queries to get optimized outputs. To store, retrieve and view the contents. To generate report based on customized need 

Course Outcome 

C01: Apply the Conepts of EntityRelationship (ER) model for the given application. CO2: Apply Normalization principles to create and maniplulate relational databases CO3: Apply the concepts of NonRelational Models CO4: Examine different file organization concepts for data storage in Relational databases CO5: Apply the transaction management principles on relational databases 
Unit1 
Teaching Hours:9 
INTRODUCTION AND CONCEPTUAL MODELING


Introduction to File and Database systems Database system structure – Data Models – Introduction to Network and Hierarchical Models – ER model – Relational Model – Relational Algebra.  
Unit2 
Teaching Hours:9 
RELATIONAL MODEL


SQL – Data definition Queries in SQL Updates Views – Integrity and Security – Relational Database design – Functional dependencies and Normalization for Relational Databases (up to BCNF).  
Unit3 
Teaching Hours:9 
NON RELATIONAL MODEL


Introduction to NOSQL Systems ,The CAP Theorem, DocumentBased NOSQL Systems and MongoDB, NOSQL KeyValue Stores, ColumnBased or Wide Column NOSQL Systems, NOSQL Graph Databases and Neo4j  
Unit4 
Teaching Hours:9 
DATA STORAGE AND QUERY PROCESSING


Record storage and Primary file organization Secondary storage Devices Operations on FilesHeap File Sorted Files Hashing Techniques – Index Structure for files –Different types of Indexes BTree  B+ Tree – Query Processing.  
Unit5 
Teaching Hours:9 
TRANSACTION MANAGEMENT


Transaction management Transaction Processing – Introduction Need for Concurrency control Desirable properties of Transaction Schedule and Recoverability Serializability and Schedules – Concurrency Control – Types of Locks Two Phases locking Deadlock Time stamp based concurrency control – Recovery Techniques – Concepts Immediate Update Deferred Update  Shadow Paging.  
Text Books And Reference Books: Abraham Silberschatz, Henry F. Korth and S. Sudarshan “Database System Concepts”, Seventh Edition, McGrawHill, 2021  
Essential Reading / Recommended Reading Andreas Meier · Michael Kaufmann "SQL & NoSQL Databases", Springer 2019 Raghu Ramakrishnan, “Database Management System”, Tata McGrawHill Publishing Company, 2003 Online Resources: W1.http://dbbook.com/db6/slidedir  
Evaluation Pattern CIA:70/100 ESE:30/100  
CS332P  DATA STRUCTURES AND ALGORITHMS (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To understand the basic concept of data structures for storage and retrieval of ordered or unordered data. Data structures include: arrays, linked lists, binary trees, heaps, and hash tables. 

Course Outcome 

CO1: Implement various ADT and Calculate the complexity of the algorithm CO2: Experiment with various operations on Linear Data structures CO3: Experiment with various Non Linear Data structures and Hashing techniques CO4: Compare different sorting techniques with respect to time complexity CO5: Make use of graph algorithms in various applications of graph traversal, shortest path and sorting techniques. 
Unit1 
Teaching Hours:8 
INTRODUCTION and STACK ADT


Definition Classification of data structures: primitive and nonprimitive Operations on data structures Algorithm Analysis: Introduction. The Stack ADT: Definition, Array representation of stack, Operations on stack: Infix, prefix and postfix notations Conversion of an arithmetic Expression from Infix to postfix. Applications of stacks.  
Unit2 
Teaching Hours:11 
LISTS AND QUEUES


The Queue ADT: Definition, Array representation of queue, Types of queues: Simple queue, circular queue, double ended queue (dequeue) priority queue, operations on all types of Queues. The List ADT: singly linked list implementation, insertion, deletion and searching operations on linear list, circular linked list implementation, Double linked list implementation, insertion, deletion and searching operations. Applications of linked lists.  
Unit3 
Teaching Hours:10 
TREES


Preliminaries – Binary Trees – The Search Tree ADT – Binary Search Trees – AVL Trees – Tree Traversals – Hashing – General Idea – Hash Function – Separate Chaining – Open Addressing –Linear Probing – Priority Queues (Heaps) – Model – Simple implementations – Binary Heap  
Unit4 
Teaching Hours:8 
SORTING


Preliminaries – Insertion Sort, Selection sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting  
Unit5 
Teaching Hours:8 
GRAPHS


Introduction to Graphs, Definitions –DFS, BFS, Minimum Spanning Tree – Prim’s and Kruskal's Algorithm. SingleSource Shortest Paths – BellmanFord algorithm and Dijkstra’s Algorithm – – Applications of Graphs  
Text Books And Reference Books: T1. Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd Edition, Pearson Education 2013.  
Essential Reading / Recommended Reading R1. Fundamentals of data structure in C by Ellis Horowitz, Sarataj Shani 3rd edition, Galgotia book source PVT,2010. R2.Classic Data Structures , Debasis Samanta ,2nd Edition, PHI Learning PVT,2011  
Evaluation Pattern Continuous Internal Assessment 70% End Semester Examination 30%  
CS333  SOFTWARE ENGINEERING (2021 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Course Description: Software engineering course provides: Different life cycle models, Requirement dictation process, Analysis modelling and specification, Architectural and detailed design methods, Implementation and testing strategies, Verification and validation techniques, Project planning and management and Use of CASE tools. Course objectives: To be aware of Different life cycle models; Requirement dictation process; Analysis modeling and specification; Architectural and detailed design methods; Implementation and testing strategies; Verification and validation techniques; Project planning and management and Use of CASE tools. 

Course Outcome 

CO1: Explain the fundamental of Software development Life cycle and different software development process models.(L1) CO2: Describe various requirement elicitation methods in software development process(L1) CO3: Choose the software processes and concepts using various design techniques(L3) CO4: Make use of different testing techniques and maintenance principles in software development process.(L3) CO5: Identify the cost estimation techniques and project scheduling methods in software development Process.(L3) 
Unit1 
Teaching Hours:9 
SOFTWARE PROCESS


Introduction –S/W Engineering Paradigm – life cycle models (waterfall, incremental, spiral, WINWIN spiral, evolutionary, prototyping, objectoriented)  system engineering – computerbased system – verification – validation – life cycle process – development process –system engineering hierarchy.  
Unit2 
Teaching Hours:9 
SOFTWARE REQUIREMENTS


Functional and nonfunctional  user – System –requirement engineering process – feasibility studies – requirements – elicitation – validation and management – software prototyping – prototyping in the software process – rapid prototyping techniques – user interface prototyping S/W document. Agile methods, Extreme Programming, SCRUM.  
Unit3 
Teaching Hours:9 
DESIGN CONCEPTS AND PRINCIPLES


Design process and concepts – Modular design – design heuristic – design model and document. Architectural design – software architecture – data design – architectural design – transform and transaction mapping – user interface design – user interface design principlesModel representation Real time systems  Real time software design – system design – real time executives – data acquisition system  monitoring and control system. SCM – Need for SCM – Version control – Introduction to SCM process – Software configuration items.  
Unit4 
Teaching Hours:9 
TESTING


Taxonomy of software testing – levels – test activities – types of s/w test – black box testing – testing boundary conditions – structural testing – test coverage criteria based on data flow mechanisms – regression testing – testing in the large. S/W testing strategies – strategic approach and issues  unit testing – integration testing – validation testing – system testing and debugging.  
Unit5 
Teaching Hours:9 
SOFTWARE PROJECT MANAGEMENT


Measures and measurements – S/W complexity and science measure – size measure – data and logic structure measure – information flow measure. Software cost estimation – function point models – COCOMO model Delphi method. Defining a Task Network – Scheduling – Earned Value Analysis – Error Tracking  Software changes – program evolution dynamics – software maintenance – Architectural evolution. Taxonomy of CASE tools – Agile based Case Study.  
Text Books And Reference Books: T1. Roger S. Pressman, Bruce Maxim, Software engineering A Practitioner’s Approach, McGrawHill International Edition, 9th Edition 2020.  
Essential Reading / Recommended Reading R1. Roger S. Pressman, Software engineering A Practitioner’s Approach, McGrawHill International Edition, 6th Edition 2014. R2. Ian Sommerville, “Software engineering,” Pearson education Asia, 9th Edition, 2013. R3. Pankaj Jalote “An Integrated Approach to Software Engineering,” Narosa Publishing house, 2011. R4.James F Peters and Witold Pedryez, “Software Engineering – An Engineering Approach”, John Wiley and Sons, New Delhi, 2010. R5.Ali Behforooz and Frederick J Hudson, “Software Engineering Fundamentals”, OUP India 2012.  
Evaluation Pattern Continuous Internal Assessment CIA Marks 50 End Semester Exams ESE 50 Total 100  
CSHO331AIP  STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

● Discuss the core concepts Statistical Analytics and Data manipulation ● Apply the basic principles, models, and algorithms supervised and unsupervised learning mechanisms. ● Analyse the structures and algorithms of regression methods ● Analyse the use of SVM in Data Science Explain notions and theories associated to Convolutional Neural Networks 

Course Outcome 

Unit1 
Teaching Hours:9 
STATISTICAL ANALYTICS AND DATA MANIPULATION


Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.  
Unit2 
Teaching Hours:9 
TECHNIQUES FOR SUPERVISED AND UNSUPERVISED LEARNING


The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis.  
Unit3 
Teaching Hours:9 
NEURAL NETWORKS


Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, 0 Computational Considerations.  
Unit4 
Teaching Hours:9 
SUPPORT VECTOR MACHINES AND FLEXIBLE DISCRIMINANTS


Introduction, The Support Vector Classifier, Support Vector Machines and Kernels, Generalizing Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis  
Unit5 
Teaching Hours:9 
RANDOM FORESTS AND ENSEMBLE LEARNING


Definition of Random Forests, Details of Random Forests Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.  
Text Books And Reference Books: T1. Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2017. T2. Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.  
Essential Reading / Recommended Reading R1. Ghahramani, Zoubin. "Probabilistic Machine Learning and Artificial Intelligence." Nature521.7553 (2015): 452. R2. Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018. R3. Wu, James, and Stephen Coggeshall. Foundations of predictive analytics. Chapman and Hall/CRC, 2012. R4. Marcoulides, George A., and Scott L. Hershberger. Multivariate statistical methods: A first course. Psychology Press, 2014. R5. Morgan, George A., et al. IBM SPSS for introductory statistics: Use and interpretation. Routledge, 2012  
Evaluation Pattern CIA 70 marks ESE 30 marks  
CSHO331CSP  PROBABILITY AND RANDOM PROCESS (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

After learning the course for a semester, the student will be aware of the important statistical information for addressing cryptography, error correction and coding, information theory and cryptanalysis. The student would also get a clear idea on some of the cases with their analytical studies in information coding and its related fields. 

Course Outcome 

CO1: To define pattern searching algorithms for different applications CO2: To classify vulnerability of subsystem based on the information gathered from different resources CO3: To estimate different optimized process and models CO4: To provide means to find the similarities between the applications and vulnerabilities of the subsystem/system CO5: To analyze about best possible patterns to cluster the possible solutions for different vulnerabilities 
Unit1 
Teaching Hours:9 
Unit 1


Probability Fundamentals, Bayes’ rule, Markov chains and application to pattern search algorithms, Classical statistical inference, Bayesian statistical inference, Regression techniques  
Unit2 
Teaching Hours:9 
Unit 2


Information coding, Pseudorandom number generators, discrete random variables, special distributions and mixed random variables, link and rank analysis , probability bounds, limiting theorem and convergence  
Unit3 
Teaching Hours:9 
unit 3


Risk M Basics of statistical learning: models, regression, curse of dimensionality, overfitting, etc. Optimization and convexity, Gradient descent, Newton’s method  
Unit4 
Teaching Hours:9 
unit 4


Classification and similarity analysis, linear discriminative analysis, regression analysis, iterative permutation analysis, Support vector machines, nearest neighbor and application of entropy  
Unit5 
Teaching Hours:9 
unit 5


Clustering algorithms, graph analysis, pattern detection, Knowledge driven system design, learning with errors, Basics of neural networks  
Text Books And Reference Books: T1. Gnedenko, Boris V. Theory of probability. Routledge, 2018. T2. Beichelt, Frank. Applied Probability and Stochastic Processes. Chapman and Hall/CRC, 2016. T3. Li, X. Rong. Probability, random signals, and statistics. CRC press, 2017  
Essential Reading / Recommended Reading R1. Grimmett, Geoffrey, Geoffrey R. Grimmett, and David Stirzaker. Probability and random processes. Oxford university press, 2001. R2. Papoulis, Athanasios, and S. Unnikrishna Pillai. Probability, random variables, and stochastic processes. Tata McGrawHill Education, 2002. R3. Rozanov, Yu. Probability theory, random processes and mathematical statistics. Vol. 344. Springer Science & Business Media, 2012.  
Evaluation Pattern CIA 50 marks ESE 50 marks  
CSHO331DAP  STATISTICAL FOUNDATION FOR DATA ANALYTICS (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

● Discuss the core concepts Statistical Analytics and Data manipulation ● Apply the basic principles, models, and algorithms supervised and unsupervised learning mechanisms. ● Analyse the structures and algorithms of regression methods ● Analyse the use of SVM in Data Science Explain notions and theories associated to Convolutional Neural Networks 

Course Outcome 

Unit1 
Teaching Hours:9 
STATISTICAL ANALYTICS AND DATA MANIPULATION


Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.  
Unit2 
Teaching Hours:9 
TECHNIQUES FOR SUPERVISED AND UNSUPERVISED LEARNING


The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis.  
Unit3 
Teaching Hours:9 
NEURAL NETWORKS


Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, 0 Computational Considerations.  
Unit4 
Teaching Hours:9 
SUPPORT VECTOR MACHINES AND FLEXIBLE DISCRIMINANTS


Introduction, The Support Vector Classifier, Support Vector Machines and Kernels, Generalizing Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis  
Unit5 
Teaching Hours:9 
RANDOM FORESTS AND ENSEMBLE LEARNING


Definition of Random Forests, Details of Random Forests Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.  
Text Books And Reference Books: T1. Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2017. T2. Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.  
Essential Reading / Recommended Reading R1. Ghahramani, Zoubin. "Probabilistic Machine Learning and Artificial Intelligence." Nature521.7553 (2015): 452. R2. Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018. R3. Wu, James, and Stephen Coggeshall. Foundations of predictive analytics. Chapman and Hall/CRC, 2012. R4. Marcoulides, George A., and Scott L. Hershberger. Multivariate statistical methods: A first course. Psychology Press, 2014. R5. Morgan, George A., et al. IBM SPSS for introductory statistics: Use and interpretation. Routledge, 2012  
Evaluation Pattern CIA 70 Marks ESE 30 marks  
CY321  CYBER SECURITY (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 

This mandatory course is aimed at providing a comprehensive overview of the different facets of Cyber Security. In addition, the course will detail into specifics of Cyber Security with Cyber Laws both in Global and Indian Legal environments 

Course Outcome 

CO1: Describe the basic security fundamentals and cyber laws and legalities CO2: Describe various cyber security vulnerabilities and threats such as virus, worms, online attacks, Dos and others. CO3: Explain the regulations and acts to prevent cyberattacks such as Risk assessment and security policy management. CO4: Explain various vulnerability assessment and penetration testing tools. CO5: Explain various protection methods to safeguard from cyberattacks using technologies like cryptography and Intrusion prevention systems. 
Unit1 
Teaching Hours:6 
UNIT 1


Security Fundamentals4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security.Cyber Laws, IT Act 2000IT Act 2008Laws for CyberSecurity, Comprehensive National CyberSecurity Initiative CNCI – Legalities  
Unit2 
Teaching Hours:6 
UNIT 2


Cyber Attack and Cyber Services Computer Virus – Computer Worms – Trojan horse.Vulnerabilities  Phishing  Online Attacks – Pharming  Phoarging – Cyber Attacks  Cyber Threats  Zombie stuxnet  Denial of Service Vulnerabilities  Server HardeningTCP/IP attackSYN Flood  
Unit3 
Teaching Hours:6 
UNIT 3


Cyber Security Management Risk Management and Assessment  Risk Management Process  Threat Determination Process Risk Assessment  Risk Management Lifecycle.Security Policy Management  Security Policies  Coverage Matrix Business Continuity Planning  DisasterTypes  Disaster Recovery Plan  Business Continuity Planning Process  
Unit4 
Teaching Hours:6 
UNIT 4


Vulnerability  Assessment and Tools: Vulnerability Testing  Penetration Testing Black box white box.Architectural Integration: Security Zones  Devicesviz Routers, Firewalls, DMZ. Configuration Management  Certification and Accreditation for CyberSecurity.  
Unit5 
Teaching Hours:6 
UNIT 5


Authentication and Cryptography: Authentication  Cryptosystems  Certificate Services, Securing Communications: Securing Services  Transport – Wireless  Steganography and NTFS Data Streams. Intrusion Detection and Prevention Systems: Intrusion  Defense in Depth  IDS/IPS IDS/IPS Weakness and Forensic AnalysisCyber Evolution: Cyber Organization – Cyber Future  
Text Books And Reference Books: R1. Matt Bishop, “Introduction to Computer Security”, Pearson, 6^{th} impression, ISBN: 9788177584257. R2. Thomas R, Justin Peltier, John, “Information Security Fundamentals”, Auerbach Publications. R3. AtulKahate, “Cryptography and Network Security”, 2^{nd} Edition, Tata McGrawHill.2003 R4. Nina Godbole, SunitBelapure, “Cyber Security”, Wiley India 1^{st} Edition 2011 R5. Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, “Cyber Security Policy Guidebook”, Wiley; 1 edition , 2012 R6. Dan Shoemaker and Wm. Arthur Conklin, “Cyber security: The Essential Body Of Knowledge”, Delmar Cengage Learning; 1 edition, 2011 R7. Stallings, “Cryptography & Network Security  Principles & Practice”, Prentice Hall, 6th Edition 2014  
Essential Reading / Recommended Reading NIL  
Evaluation Pattern Only CIA will be conducted as per the University norms. No ESE Maximum Marks : 50  
EC337  DIGITAL SYSTEMS (2021 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

· To study the fundamentals of digital circuits and concepts used in the analysis and design of various digital systems. 

Course Outcome 

CO1: Describe the characteristics of various digital integrated circuit families, logic gates and classify digital circuits based on their construction. L2:Understand CO2: Demonstrate the methods of minimization of complex circuits using Boolean Algebra.L3: Apply CO3: Interpret the methods of Designing combinational circuit.L3: Apply CO4: Illustrate the methods of Designing sequential circuits.L3: Apply CO5: Analyze the digital circuits design using VHDL.L4:Analyze 
Unit1 
Teaching Hours:9 
INTRODUCTION


Switching Theory: Laws of Boolean algebra, Theorems of Boolean algebra, Switching functions, Methods for specification of switching functions  Truth tables and Algebraic forms, Realization of functions using logic gates. Digital Logic Elements: Electronic logic gates, Positive and negative logic, Logic families TTL, ECL and CMOS, Realization of logic gates.  
Unit2 
Teaching Hours:9 
BOOLEAN ALGEBRA


Simplification of Boolean Expressions and Functions: Algebraic methods, Canonical forms of Boolean functions, Minimization of functions using Karnaugh maps, Minimization of functions using QuineMcClusky method.  
Unit3 
Teaching Hours:9 
COMBINATIONAL CIRCUITS


Design of Combinational Logic Circuits: Gate level design of Small Scale Integration (SSI) circuits, Modular combinational logic elements  Decoders, Encoders, Priority encoders, Multiplexers and Demultiplexers. Design of Integer Arithmetic Circuits using Combinational Logic: Integer adders  Ripple carry adder and Carry look ahead adder, Integer subtractors using adders, Unsigned integer multipliers  Combinational array circuits, Signed integer multipliers  Booth's coding, Bitpair recoding, Carry save addition and Wallace tree multiplier, Signed integer division circuits  Combinational array circuits, Complexity and propagation delay analysis of circuits. Design of Combinational Circuits using Programmable Logic Devices (PLDs): Programmable Read Only Memories (PROMs), Programmable Logic Arrays (PLAs), Programmable Array Logic (PAL) devices, Design of multiple output circuits using PLDs.  
Unit4 
Teaching Hours:9 
SEQUENTIAL CIRCUITS


Sequential Circuit Elements: Latches RS latch and JK latch, FlipflopsRS, JK, T and D flip flops, Masterslave flip flops, Edgetriggered flipflops. Analysis and Design of Synchronous Sequential Circuits: Models of sequential circuits  Moore machine and Mealy machine, Flipflops  Characteristic table, Characteristic equation and Excitation table, Analysis of sequential circuits Flipflop input expressions, Next state equations, Next state maps, State table and State transition diagram, Design of sequential circuits  State transition diagram, State table, Next state maps, Output maps, Expressions for flipflop inputs and Expressions for circuit outputs, Modular sequential logic circuits Shift registers, Registers, Counters and Random access memories, Design using programmable logic sequencers (PLSs). Design of Arithmetic Circuits using Sequential Logic : Serial adder for integers, Unsigned integer multiplier, Unsigned integer division circuits, Signed integer division, Floatingpint adder/subtractor  Design of control circuit, Floating  point multiplier.  
Unit5 
Teaching Hours:9 
CASE STUDY AND INFORMAL LABORATORY


Case study: Learn the Fundamentals of Digital Logic Design with VHDL Informal Laboratory: · Design and implementation of binary adder / subtractor using basic gates · Design and implementation of applications using multiplexers · Design and implementation of Synchronous & Asynchronous Counters · Design and implementation of Shift Registers Coding Combinational Circuits using Hardware Description Language (HDL)  
Text Books And Reference Books:
T1  Donald P Leach, Albert Paul Malvino&GoutamSaha, “Digital Principles and Applications” , Tata McGraw Hill 7^{th} Edition, 2010.
 
Essential Reading / Recommended Reading R1 Stephen Brown. ZvonkoVranesic, “Fundamentals of Digital Logic Design with VHDL”, Tata McGraw Hill, 2nd Edition 2005. R2 R D Sudhaker Samuel, “Illustrative Approach to Logic Design. SanguinePearson”, 2010. R3 Charles H. Roth, “Fundamentals of Logic Design”, Cengage Learning, 5th Edition, 2004. R4 Ronald J. Tocci, Neal S. Widmer. Gregory L. Moss, “Digital Systems Principles and Applications, ” 10^{th} Edition. Pearson Education, 2007 R5 M Morris Mano, “Digital Logic and Computer Design”, Pearson Education, 10th Edition, 2008.
 
Evaluation Pattern Assessment is based on the performance of the student throughout the semester. Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks For subjects having practical as part of the subject
Assessment of Practical paper Conduct of experiments : 25 marks Observations/Lab Record : 15 marks Viva voce : 10 marks Total : 50 marks (All the above assessments are carried for each experiment during regular lab classes and averaged to max 50 marks at the end of the semester)  
HS311  TECHNICAL WRITING (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

Course description: The course Technical Writing consists; Introduction to Technical Communication, Technical Writing, Soft Skills, Professional Presentation Skills and Professional Etiquette. It provides awareness and practice to the learners in all aspects required for effective technical writing. Course objective: This course aims to equip engineering students with effective individual and collaborative technical writing and presentation skills which are necessary to be effective technical communicators in academic and professional contexts. 

Course Outcome 

CO1: Understand the basics of technical communication and the use of formal elements of specific genres of documentation CO2: Demonstrate the nuances of technical writing, with reference to English grammar and vocabulary. CO3: Recognize the importance of soft skills and personality development for academic and professional success. CO4: Understand various techniques involved in oral communication and its application in the professional contexts. CO5: Realize the importance of having ethical work habits and professional etiquettes. 
Unit1 
Teaching Hours:6 

Introduction to Technical Communication


Communication Process, Flow, Barriers. Analyzing different kinds of technical documents, Reports/Engineering reports. Types, Importance and Structure of formal reports, information and document design  
Unit2 
Teaching Hours:6 

Technical Writing


Vocabulary for professional writing. Idioms and collocations. Writing drafts and revising, writing style and language. Writing Emails, Resumes, Video resume, Interviews, Types of interviews.  
Unit3 
Teaching Hours:6 

Soft Skills


Self development process, Personality development, Types of personality, Perception and attitudes, Emotional intelligence, Time Management, Values and beliefs, Personal goal setting, Creativity, Conflict management, Career planning  
Unit4 
Teaching Hours:6 

Professional Presentation Skills


Writing a speech, Formal presentations, Public speaking, Presentation aids, Group communication, Discussions, Organizational GD, Meetings & Conferences.  
Unit5 
Teaching Hours:6 

Professional Etiquette


Email etiquettes, Telephone Etiquettes, Engineering ethics, Role and responsibility of engineer, Work culture in jobs.  
Text Books And Reference Books: T1: David F. Beer and David McMurrey, Guide to writing as an Engineer, John Willey. New York, 2004 T2: Diane Hacker, Pocket Style Manual, Bedford Publication, New York, 2003. (ISBN 0312406843) T3: Raman Sharma, Technical Communications, Oxford Publication, London, 2004  
Essential Reading / Recommended Reading R1. Dale Jungk, Applied Writing for Technicians, McGraw Hill, New York, 2004. (ISBN: 078283574) R2. Sharma, R. and Mohan, K. Business Correspondence and Report Writing, TMH New Delhi 2002. R3. Xebec, Presentation Book, TMH New Delhi, 2000. (ISBN 0402213)
W1. Watch Hans Rosling’s presentation on TED Talks: “The best stats you’ve ever seen.” Watch the opening to this presentation.
W2. Use your search engine and search for “The Brand Called You.” The result you're looking for should be from the Fast Company website
 
Evaluation Pattern
 
MA334  DISCRETE MATHEMATICS (2021 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

The course is to: ● Develop the knowledge of the concepts needed to test the logic of a program. ● Create logical knowledge which has applications in Data Structures, Analysis of Algorithms, Programming languages, Theory of Compilers, Artificial Intelligence and Database ● Identify structures on discrete levels ● Construct relation matrix and graph , define partially ordered set and Boolean algebra ● Classify types of error correction in coding and decoding using binary digits ● Interpret the concepts and properties of algebraic structures such as semigroups, monoids and groups. 

Course Outcome 

CO1: Distinguish the compound logical statements with logical connectives {L2} {PO1, PO2} CO2: Apply the rules of inference and Predicate/Quantifiers to validate the set of arguments. {L3} {PO1, PO2, PO3} CO3: Analyze Lattices and Boolean algebra by using the concept of bounded and partial order set. {L4} {PO1, PO2, PO3} CO4: Predict permutation functions as even or odd and solve on inverse functions {L3} {PO1, PO2, PO3, PO10} CO5: Compute coding and decoding problems using group theory and appropriate coding and decoding schemes. {L3} {PO1, PO2, PO3, PO10} 
Unit1 
Teaching Hours:9 

Propositional Calculus:


Propositions Logical connectives Compound propositions Conditional and bi conditional propositions Truth tables Tautologies and contradictions Contrapositive Logical equivalences and implications De Morgans Laws  Normal forms Principal conjunctive and disjunctive normal forms Rules of inference Arguments  Validity of arguments.  
Unit2 
Teaching Hours:9 

Predicate Calculus:


Predicates, Statement Function , Variables  Free and bound variables , Quantifiers , Universe of discourse, Logical equivalences and implications for quantified statements , Theory of inference, The rules of universal specification and generalization , Validity of arguments  
Unit3 
Teaching Hours:9 

Set Theory


Basic concepts Notations Subset Algebra of sets The power set Ordered pairs and Cartesian product Relations on sets Types of relations and their properties Matrix and Graph representation of a relation Partial ordering Poset Hasse diagram Lattices and their properties Sublattices Boolean algebra.  
Unit4 
Teaching Hours:9 

Functions:


Definitions of functions Classification of functions Types of functions  Examples Composition of functions Inverse functions Characteristic function of a set Hashing functions Permutation functions.  
Unit5 
Teaching Hours:9 

Groups:


Groups  Properties Subgroups  Cosets and Lagranges theorem Normal subgroups Algebraic system with two binary operations Preliminaries of Coding  Hamming Metric  group codes: Basic notions of error correction  Error recovery in group codes.  
Text Books And Reference Books: Text Books T1. Trembly J.P and Manohar R, Discrete Mathematical Structures with Applications to Computer Science, Tata McGrawHill Pub.Co. Ltd, New Delhi, 2003. T2. Ralph. P. Grimaldi, Discrete and Combinatorial Mathematics: An Applied Introduction, Fifth Edition, Pearson Education Asia,Delhi, 2009.  
Essential Reading / Recommended Reading Reference Books 1. R1. Bernard Kolman, Robert C. Busby, Sharan Cutler Ross, Discrete Mathematical Structures, Fourth Indian reprint, Pearson Education Pvt Ltd., New Delhi, 2003. 2. R2. Kenneth H. Rosen, Discrete Mathematics and its Applications, Fifth Edition, Tata McGraw Hill Pub. Co. Ltd., New Delhi, 2003. 3. R3. Richard Johnsonbaugh, Discrete Mathematics, Fifth Edition,Pearson Education Asia, New Delhi, 2002.  
Evaluation Pattern Continuous Internal Assessment (CIA): 50% (50 marks out of 100 marks) End Semester Examination(ESE) : 50% (50 marks out of 100 marks)
Components of the CIA CIA I : Subject Assignments / Online Tests : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIAIII:Quiz/Seminar/Case Studies/Project/Innovative Assignments/presentations/publications : 10 marks Attendance : 05 marks Total : 50 marks Mid Semester Examination (MSE) : Theory Papers:
End Semester Examination (ESE): The ESE is conducted for 100 marks of 3 hours duration. The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution. Question paper pattern is as follows. Two full questions with either or choice will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is to test the objectiveness, analytical skill and application skill of the concept, from a question bank which reviewed and updated every year The criteria for drawing the questions from the Question Bank are as follows 50 %  Medium Level questions 25 %  Simple level questions 25 %  Complex level questions  
BS451  ENGINEERING BIOLOGY LABORATORY (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

Understanding and application of MATLAB and TINKERCAD for biological analysis which would results in better healthcare and any engineer, irrespective of the parent discipline (mechanical, electrical, civil, computer, electronics, etc.,) can use the disciplinary skills toward designing/improving biological systems. This course is designed to convey the essentials of human physiology.
The course will introduce to the students the various fundamental concepts in MATLAB and TINKERCAD for numerical analysis and circuit design using arduino.


Course Outcome 

CO1: Perform basic mathematical operation and analysis on biological parameters as BMI, ECG using MATLAB.L4 CO2: Perform basic image processing on RGB images pertaining to medical data using MATLAB. L4 CO3: Perform analysis on biological parameters using TinkerCad and design mini projects applicable for healthcare and biosensing.L4 
Unit1 
Teaching Hours:30 
LIST OF EXPERIMENTS


1. To familiarize with Matlab Online and getting used to basic functionalities used in Matlab (arrays, matrices, tables, functions) 2. To calculate the Body Mass Index (BMI) of a person and determine under what category the person falls under – underweight, normal, overweight 3. To determine the R peaks in given ECG and to find HRV using Matlab. 4. To determine the R peaks in given ECG and to find HRV using Matlab. 5. To determine the R peaks in given ECG and to find HRV using Matlab. 6. Introduction to Tinkercad and using the various tools available for running a simple program of lighting a LED bulb using Arduino (digital). 7. To design a driver motor in Tinkercad using Arduino and driver motor 8. To design a temperature sensor in Tinkercad using Arduino and TMP36 9. To design and simulate gas sensors using potentiometers, Arduino and servo motors 10. To design and simulate measuring pulse sensors using photodiodes, IR LED and Arduino 11. Preparation of biopolymers (polylactic acid) at home using homebased ingredients.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern As per university norms  
CS432P  OPERATING SYSTEMS (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course objectives: This course is an overview of different types of operating systems. They also include understanding of the components of an operating system, process management, and knowledge of storage management and the concepts of I/O and file systems is also covered as an introductory level. 

Course Outcome 

CO1: Demonstrate the Structure, Components and its basic functionalities of Operating System CO2: Distinguish various process management principles for given problem using appropriate tool CO3: Elucidate the process synchronization mechanisms, deadlock environment and its solutions in the given processes CO4: Inspect various memory management strategies for the given problems in memory systems CO5: Build file structure to distribute the same across the memory 
Unit1 
Teaching Hours:9 

OBJECTORIENTED PROGRAMMING  FUNDAMENTALS


Review of OOP  Objects and classes in Java – defining classes – methods  access specifiers – static members – constructors – finalize method – Arrays – Strings  Packages – JavaDoc comments.  
Unit2 
Teaching Hours:9 

OBJECTORIENTED PROGRAMMING  INHERITANCE


Inheritance – class hierarchy – polymorphism – dynamic binding – final keyword – abstract classes – the Object class – Reflection – interfaces – object cloning – inner classes.  
Unit3 
Teaching Hours:9 

EVENTDRIVEN PROGRAMMING


Graphics programming – Frame – Components – working with 2D shapes – Using color, fonts, and images  Basics of event handling – event handlers – adapter classes – actions – mouse events – AWT event hierarchy – introduction to Swing – ModelView Controller design pattern – buttons – layout management – Swing Components  
Unit4 
Teaching Hours:9 

GENERIC PROGRAMMING


Motivation for generic programming – generic classes – generic methods – generic code and virtual machine – inheritance and generics – reflection and generics – exceptions – exception hierarchy – throwing and catching exceptions.  
Unit5 
Teaching Hours:9 

CONCURRENT PROGRAMMING


Multithreaded programming – interrupting threads – thread states – thread properties – thread synchronization – threadsafe Collections – Executors – synchronizers – threads and eventdriven programming, Parallel programming –fork, join framework.  
Text Books And Reference Books: 1. Herbert Schildt, “Java The Complete Reference” , Ninth Edition, McGraw Hill Publishers 2014. 2. Cay S. Horstmann and Gary Cornell, “Core Java: Volume I – undamentals”, Eighth Edition, Sun Microsystems Press, 2008.  
Essential Reading / Recommended Reading 1. Paul Deitel and Harvey Deitel , “Java How to program”, Tenth Edition, Deitel, 2016. 2. Ivan BratikoPROLOG: Programming for Artificial Intelligence, Third Edition, Pearson Publisher, 2002. 3. Bruce Eckel, “Thinking in Java”, 4th Edition, February 20, 2006. 4. Doug Rosenberg, Matt Stephens, “Use Case Driven Object Modeling with UML: Theory and Practice (Expert's Voice in UML Modeling)”, January 16, 2013.  
Evaluation Pattern
 
CS434  FORMAL LANGUAGE AND AUTOMATA THEORY (2021 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 



Course Outcome 

CO1: Design finite automata with conversion between types of finite automata. CO2: Develop regular expression and minimize the given finite automata for any regular language. CO3: Develop context free grammar, parse trees and pushdown automata for a given context free language. CO4: Explain decidable and undecidable problems, solvable and unsolvable problems with their complexity analysis. 
Unit1 
Teaching Hours:9 
AUTOMATA


Automata  Introduction to formal proof – Additional forms of proof – Inductive proofs –Finite Automata (FA) – Central concepts of Automata Theory, Representation of Automata, Deterministic Finite Automata (DFA) – Nondeterministic Finite Automata (NFA) – Finite Automata with Epsilon transitions. Introduction to automata simulation tools  
Unit2 
Teaching Hours:9 
REGULAR EXPRESSIONS AND LANGUAGES


Regular Expression – FA and Regular Expressions – Proving languages not to be regular – Closure properties of regular languages – Equivalence and minimization of Automata.  
Unit3 
Teaching Hours:9 
ContextFree Grammar and Languages


ContextFree Grammar (CFG) – LMD, RMD, Parse Trees – Ambiguity in grammars and languages – Definition of the Pushdown automata – Languages of a Pushdown Automata, Designing of a PDA and string acceptance – Equivalence of Pushdown automata and CFG, NonDeterministic Pushdown Automata.  
Unit4 
Teaching Hours:9 
Properties of ContextFree Languages


Simplifications of CFG, Normal forms for CFG – Pumping Lemma for CFL  Closure Properties of CFL – Turing Machines – Definition, Problems, Language accepted, String acceptance, Programming Techniques for TM.  
Unit5 
Teaching Hours:9 
Undecidability


A language that is not Recursively Enumerable (RE) – An undecidable problem that is RE – Undecidable problems about Turing Machine – Post’s Correspondence Problem  The classes P and NP, Linear Bounded Automata  Definition and examples  
Text Books And Reference Books: Text Books: T1. J.E.Hopcroft, R.Motwani and J.D Ullman, “Introduction to Automata Theory, Languages and Computations”, Pearson Education, 2008. T2. Peter Linz “An Introduction to formal languages and automata”, sixth edition, Jones and Bartlett Learning, 2016.  
Essential Reading / Recommended Reading Reference Books:
R1. H.R.Lewis and C.H.Papadimitriou, “Elements of The theory of Computation”, Second Edition, Pearson Education/PHI, 2003 R2. J.Martin, “Introduction to Languages and the Theory of Computation”, 3rd Edition, TMH, 2003. R3. MichealSipser, “Introduction of the Theory and Computation”, Thomson Brokecole, 3rd Edition, 1997  
Evaluation Pattern CIA = 50% out of 100 ESE = 50% out of 100  
CS435P  COMPUTER ORGANIZATION AND ARCHITECTURE (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course will help the students to learn about basic structure of computer system, design of arithmetic and logic unit with the implementation of fixed and floating point numbers. Further, it will give knowledge about design of control unit and pipelined processing concepts. It discusses about various parallel processing architectures, different memory systems and I/O Communication systems 

Course Outcome 

CO1: Demonstrate the functions of basic components of computer system and Instruction set Architecture CO2: Select suitable arithmetic algorithm to solve given arithmetic and logical problems CO3: Utilize appropriate instruction level parallelism concepts in multiprocessing environment CO4: Identify suitable control unit design and pipelining principles in computer architecture design CO5: Choose suitable memory and I/O system design 
Unit1 
Teaching Hours:9 
FUNDAMENTALS OF COMPUTER SYSTEM


Functional Units – Basic Operational Concepts – Performance – Instructions: Language of the Computer – Operations, Operands – Instruction representation – Logical operations – decision making – MIPS Addressing.  
Unit2 
Teaching Hours:9 
COMPUTER ARTHIMETIC


Addition and Subtraction – Multiplication – Division – Floating Point Representation – Floating Point Operations – Subword Parallelism  
Unit3 
Teaching Hours:9 
BASIC PROCESSING AND CONTROL UNIT


A Basic MIPS implementation – Building a Datapath – Control Implementation Scheme – Pipelining – Pipelined data path and control – Handling Data Hazards & Control Hazards – Exceptions.  
Unit4 
Teaching Hours:9 
PARALLELISM


Parallel processing challenges – Flynn‘s classification – SISD, MIMD, SIMD, SPMD, and Vector Architectures  Hardware multithreading – Multicore processors and other Shared Memory Multiprocessors  Introduction to Graphics Processing Units, Clusters, Warehouse Scale Computers and other MessagePassing Multiprocessors.  
Unit5 
Teaching Hours:9 
MEMORY AND I/O


Memory Hierarchy  memory technologies – cache memory – measuring and improving cache performance – virtual memory, TLB‘s – Accessing I/O Devices – Interrupts – Direct Memory Access – Bus structure – Bus operation – Arbitration – Interface circuits  USB.  
Text Books And Reference Books: Text Books: T1. David A. Patterson and John L. Hennessy, “Computer Organization and Design: The Hardware/Software Interface”, Fifth Edition, Morgan Kaufmann / Elsevier, 2014. T2. Carl Hamacher, ZvonkoVranesic, SafwatZaky and NaraigManjikian, “Computer Organization and Embedded Systems”, Sixth Edition, Tata McGraw Hill, 2012.  
Essential Reading / Recommended Reading Reference Books: R1. William Stallings, “Computer Organization and Architecture – Designing for Performance”, Sixth Edition, Pearson Education, 2003. R2. John L. Hennessey and David A. Patterson, “Computer Architecture –A Quantitative Approach”, Fifth Edition, Morgan Kaufmann / Elsevier Publishers,2012.  
Evaluation Pattern CIA  70% out of 100 ESE  30% out of 100  
CSHO432AIP  ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

● Discuss the core concepts Statistical Analytics and Regression ● Apply the basic principles, models, and algorithms for Multiple and Non Linear Regression ● Analyze the structures and algorithms of Convolutional Neural Networks ● Explain notions and theories associated to Convolutional Neural Networks
● Solve problems in Deep Unsupervised Learning 

Course Outcome 

CO1: Understand and explain concepts associated Statistical Analytics and Regression CO2: Infer details of Multiple and Non Linear Regression mechanisms. CO3: Solve problems connected to Convolutional Neural Networks. CO4: Analyse concepts of Convolutional Neural Networks. CO5: Appraise concepts of Deep Unsupervised Learning. 
Unit1 
Teaching Hours:9 
Regression


Relationship between attributes using Covariance and Correlation, Relationship between multiple variables: Regression (Linear, Multivariate) in prediction. Residual Analysis, Identifying significant features, feature reduction using AIC, multicollinearity, Nonnormality and Heteroscedasticity  
Unit2 
Teaching Hours:9 
Multiple and Non Linear Regression


Polynomial Regression, Regularization methods, Lasso, Ridge and Elastic nets, Categorical Variables in Regression, Logit function and interpretation, Types of error measures (ROCR), Logistic Regression in classification  
Unit3 
Teaching Hours:9 
Convolutional Neural Networks I


Invariance, stability. Variability models (deformation model, stochastic model). Scattering networks, Group Formalism, Supervised Learning: classification. Properties of CNN representations: invertibility, stability, invariance. Covariance/invariance: capsules and related models.  
Unit4 
Teaching Hours:9 
Convolutional Neural Networks II


Connections with other models: dictionary learning, LISTA. Other tasks: localization, regression. Embeddings (DrLim), inverse problems, Extensions to noneuclidean domains Dynamical systems: RNNs.  
Unit5 
Teaching Hours:9 
Deep Unsupervised Learning


Autoencoders (standard, denoising, contractive, Variational Autoencoders Adversarial Generative Networks, Maximum Entropy Distributions
 
Text Books And Reference Books: T1.Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018. T2.Sebastian Raschka and Vahid MirjaliliPython Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow 2, 3rd Edition, Packt, 2019  
Essential Reading / Recommended Reading R1. Seber, Linear Regression Analysis 2ed,Wiley India Exclusive (Cbs), 2018 R2. Jeremy Arkes, Regression Analysis: A Practical Introduction, Routledge, 2019 R3. Aurelien Geron, HandsOn Machine Learning with ScikitLearn, Keras and Tensor Flow: Concepts, Tools and Techniques to Build Intelligent Systems, Shroff/O'Reilly, 2019 R4. Andreas Muller, Introduction to Machine Learning with Python: A Guide for Data Scientists, Shroff/O'Reilly, 2016 R5. François Chollet, Deep Learning with Python, Manning Publications, 2017  
Evaluation Pattern CIA: 70 ESE:30  
CSHO432CSP  MOBILE AND NETWORK BASED ETHICAL HACKING (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

After learning the course for a semester, the student will be aware of the hacking concepts in cyber security for addressing cryptography, data protection, informationnetwork security and detection of attacks. The student would also get a clear idea on some of the cases with their analytical studies in cyberattacks and hacking in the related fields. 

Course Outcome 

CO1: To describe the vulnerability scanning for network. CO2: To understand the information gathering modes for any attack on the network CO3: To evaluate different hacking process and corresponding attacks for mobile platforms CO4: To interpret means to evade firewalls and other security parameter for ethical hacking CO5: To analyze about best possible solutions for different vulnerabilities that are exploited for hacking 
Unit1 
Teaching Hours:9 
INTRODUCTION


Introduction to ethical hacking, IP addressing, Network routing protocols, network security, network scanning, and vulnerability assessment OpenVAS, Nessus, etc. of computation device (mobile, pc, etc.) and network of the system  
Unit2 
Teaching Hours:9 
UNITII


Computation system hacking, modes of gathering information, password cracking, penetration testing including backdoor issues, Malware threats and different cyberrelated attacks  
Unit3 
Teaching Hours:9 
UNITIII


Introduction to Mobile Hacking, encryption types and attacks, different mobile platforms and corresponding vulnerabilities  
Unit4 
Teaching Hours:9 
UNITIV


Evading firewalls, standard detection systems and frameworks, and other possible ways of detecting attacks  
Unit5 
Teaching Hours:9 
UNITV


Case studies: various hacking scenarios and their information gathering along with possible solutions.  
Text Books And Reference Books: 1. Thompsons, Josh. Hacking: Hacking For Beginners Guide On How To Hack, Computer Hacking, And The Basics Of Ethical Hacking (Hacking Books). Create Space Independent Publishing Platform, 2017. 2. Weidman, Georgia. Penetration testing: a handson introduction to hacking. No Starch Press, 2014. 3. Dwivedi, Himanshu. Mobile application security. Tata McGrawHill Education, 2010  
Essential Reading / Recommended Reading 1. Engebretson, Patrick. The basics of hacking and penetration testing: ethical hacking and penetration testing made easy. Elsevier, 2013. 2. McNab, Chris. Network security assessment: know your network. " O'Reilly Media, Inc.", 2007. 3. Simpson, Michael T., Kent Backman, and James Corley. Handson ethical hacking and network defense. Cengage Learning, 2010  
Evaluation Pattern CIA70 ESE30  
CSHO432DAP  BIG DATA ANALYTICS (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course objectives: To Understand big data for business intelligence To Learn business case studies for big data analytics To Understand Nosql big data management To manage Big data without SQL To understanding mapreduce analytics using Hadoop and related tools 

Course Outcome 

Unit1 
Teaching Hours:9 

Unit 1


Introduction to ethical hacking, IP addressing, Network routing protocols, network security, network scanning, and vulnerability assessment OpenVAS, Nessus, etc. of computation device (mobile, pc, etc.) and network of the systemIntroduction to ethical hacking, IP addressing, Network routing protocols, network security, network scanning, and vulnerability assessment OpenVAS, Nessus, etc. of computation device (mobile, pc, etc.) and network of the system  
Unit2 
Teaching Hours:9 

Unit 2


Computation system hacking, modes of gathering information, password cracking, penetration testing including backdoor issues, Malware threats and different cyberrelated attacks  
Unit3 
Teaching Hours:9 

Unit 3


Introduction to Mobile Hacking, encryption types and attacks, different mobile platforms and corresponding vulnerabilities  
Unit4 
Teaching Hours:9 

Unit 4


 
Unit5 
Teaching Hours:9 

Unit 5


Case studies: various hacking scenarios and their information gathering along with possible solutions.  
Text Books And Reference Books: T1. Tom White, "Hadoop: The Definitive Guide", 4^{th} Edition, O'Reilley, 2012. T2. Eric Sammer, "Hadoop Operations",1^{st} Edition, O'Reilley, 2012.  
Essential Reading / Recommended Reading Reference Books: R1. VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013. R2. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012. R3. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011. R4. Alan Gates, "Programming Pig", O'Reilley, 2011.  
Evaluation Pattern MSE  70% ESE  30%  
EVS421  ENVIRONMENTAL SCIENCE (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:0 
Credits:0 

Course Objectives/Course Description 

To understand the scope and importance of environmental science towards developing a conscious community for environmental issues, both at global and local scale. 

Course Outcome 

CO1. Explain the components and concept of various ecosystems in the environment (L2, PO7) CO2. Explain the necessity of natural resources management (L2, PO1, PO2 and PO7) CO3.Relate the causes and impacts of environmental pollution (L4, PO1, PO2, and PO3, PO4) CO4.Relate climate change/global atmospheric changes and adaptation (L4,PO7) CO5. Appraise the role of technology and institutional mechanisms for environmental protection (L5, PO8)

Unit1 
Teaching Hours:6 
Introduction


Environment and Eco systems – Definition, Scope and importance. Components of environment. Concept and Structure of eco systems. Material Cycles – Nitrogen, Carbon, Sulphur, Phosphorous, Oxygen. Energy Flow and classification of Eco systems.  
Unit2 
Teaching Hours:6 
Natural Resources


Classification and importance Forest, Water, Mineral, Food, Energy. Management of natural resources – challenges and methods. Sustainable development – Goals, Agriculture, Industries  
Unit3 
Teaching Hours:6 
Environmental Pollution


Causes and Impacts – Air pollution, Water pollution, Soil Pollution, Noise Pollution, Marine Pollution, Municipal Solid Wastes, Bio Medical and EWaste. Solid Waste Management  
Unit4 
Teaching Hours:6 
Climate change/Global Atmospheric Change


Global Temperature, Greenhouse effect, global energy balance, Global warming potential, International Panel for Climate Change (IPCC) Emission scenarios, Oceans and climate change. Adaptation methods. Green Climate fund. Climate change related planning small islands and coastal region. Impact on women, children, youths and marginalized communities  
Unit5 
Teaching Hours:6 
Environmental Protection


Technology, Modern Tools – GIS and Remote Sensing,. Institutional Mechanisms  Environmental Acts and Regulations, Role of government, Legal aspects. Role of Nongovernmental Organizations (NGOs) , Environmental Education and Entrepreneurship  
Text Books And Reference Books: T1Kaushik A and Kaushik. C. P, “Perspectives in Environmental Studies”New Age International Publishers, New Delhi, 2018 [Unit: I, II, III and IV] T2Asthana and Asthana, “A text Book of Environmental Studies”, S. Chand, New Delhi, Revised Edition, 2010 [Unit: I, II, III and V] T3Nandini. N, Sunitha. N and Tandon. S, “environmental Studies” , Sapana, Bangalore, June 2019 [Unit: I, II, III and IV] T4R Rajagopalan, “Environmental Studies – From Crisis to Cure”, Oxford, Seventh University Press, 2017, [Unit: I, II, III and IV]
 
Essential Reading / Recommended Reading R1.Miller. G. T and Spoolman. S. E, “Environmental Science”, CENAGE Learning, New Delhi, 2015 R2.Masters, G andEla, W.P (2015), Introduction to environmental Engineering and Science, 3rd Edition. Pearson., New Delhi, 2013. R3.Raman Sivakumar, “Principals of Environmental Science and Engineering”, Second Edition, Cengage learning Singapore, 2005. R4.P. Meenakshi, “Elements of Environmental Science and Engineering”, Prentice Hall of India Private Limited, New Delhi, 2006. R5.S.M. Prakash, “Environmental Studies”, Elite Publishers Mangalore, 2007 R6.ErachBharucha, “Textbook of Environmental Studies”, for UGC, University press, 2005. R7. Dr. Pratiba Sing, Dr. AnoopSingh and Dr. PiyushMalaviya, “Textbook of Environmental and Ecology”, Acme Learning Pvt. Ltd. New Delhi.  
Evaluation Pattern No Evaluation  
HS422  PROFESSIONAL ETHICS (2021 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

Understand the importance of Values and Ethics in their personal lives and professional careers 

Course Outcome 

CO1: Understand the importance of Values and Ethics in their personal lives and professional careers CO2: Learn the rights and responsibilities as an employee, team member and a global citizen CO3: Estimate the impact of self and organization?s actions on the stakeholders and society CO4: Develop an ethical behaviour under all situations CO5: Appreciate the significance of Intellectual Property as a very important driver of growth and development in today?s world and be able to statutorily acquire and use different types of intellectual property in their professional life 
Unit1 
Teaching Hours:6 
Introduction to Ethics


Introduction to Professional Ethics : Definition, Nature, Scope Moral Dilemmas moral AutonomyKohlberg’s theory Gilligan’s theory, Profession Persuasive, Definitions, Multiple motives, Models of professional goals. Moral Reasoning and Ethical theories – Professional Ideals and Virtues Theories of Right Action, Self interest, Customs and Regions Use of ethical Theories  
Unit2 
Teaching Hours:6 
Engineering as Social Experimentation and Responsibility


Engineering as Social Experimentation and Responsibility For Safety Engineering as experimentation Engineers as responsible experimenters, the challenger case, Codes of Ethics, A balanced outlook on law. Concept of safety and risk, assessment of safety and risk risk benefit analysis and reducing the risk three mile island, Chernobyl and safe exists.  
Unit3 
Teaching Hours:6 
Global Issues and Introduction To Intellectual Property


Global Issues and Introduction To Intellectual Property  Multinational corporations Environmental ethics Computer ethics and Weapons developments. Meaning and Types of Intellectual Property, Intellectual Property. Law Basics, Agencies responsible for intellectual property registration, International Organizations, Agencies and Treaties, Importance of Intellectual Property Rights.  
Unit4 
Teaching Hours:6 
Foundations of Trademarks


Foundations of Trademarks  Meaning of Trademarks, Purpose and Functions of Trademarks, types of Marks, Acquisition of Trademark rights, Common Law rights, Categories of Marks, Trade names and Business Name, Protectable Matter, Exclusions from Trademark Protection.  
Unit5 
Teaching Hours:6 
Foundations of Copyrights Law


Foundations of Copyrights Law  Meaning of Copyrights, Common Law rights and Rights under the 1976 copyright Act, Recent developments of the Copyright Act, The United States Copyright Office  
Text Books And Reference Books: T1. Mike Martin and Roland Schinzinger, “Ethics in Engineering”, McGrawHill, New York 1996. T2. Govindarajan M, Natarajan S, Senthil Kumar V. S, “Engineering Ethics”, Prentice Hall of India, New Delhi, 2004.
 
Essential Reading / Recommended Reading R1. Jayashree Suresh &B.S.Raghavan “Human values and Professional Ethics”, S. Chand, 2009.
R2. Govindarajan, Natarajan and Senthilkumar “Engineering Ethics”, PHI:009.
R3. Nagarajan “A Text Book on Professional ethics and Human values”, New Age International, 2009.
R4. Charles &Fleddermann “Engineering Ethics”, Pearson, 2009.
R5. Rachana Singh Puri and Arvind Viswanathan, I.K.”Practical Approach to Intellectual Property rights”, International Publishing House, New Delhi. 2010.
R6. A.B.Rao “Business Ethics and Professional Values”, Excel, 2009  
Evaluation Pattern CIA I Evaluated out of (20) > CIA I cnverted to (10) CIA II  Evaluated out of (50) > CIA II cnverted to ( 25) CIA III  Evaluated out of (20) > CIA III cnverted to (10) Total CIA is scaled down to 20 Att. Marks5 ESE Evaluated out of (50) > ESE converted to (25) Total marks  50  
MA431  PROBABILITY AND QUEUING THEORY (2021 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

1. At the end of the course, the students would have a fundamental knowledge of the basic probability concepts. 2. Have a well – founded knowledge of standard distributions which can describe real life phenomena. 3. Acquire skills in handling situations involving more than one random variable and functions of random variables. 4. Understand and characterize phenomena which evolve with respect to time in a probabilistic manner. 5. Be exposed to basic characteristic features of a queuing system and acquire skills in analyzing queuing models.


Course Outcome 

CO1: Understand the basic probability concepts {L2} {PO1, PO2, PO3} CO2: Describe standard distributions which can describe real life phenomena {L2} {PO1, PO2, PO3} CO3: Solve problems involving more than one random variable and functions of random variables. {L3} {PO1, PO2, PO3} CO4: Understand and characterize phenomena which evolve with respect to time in a probabilistic manner. {L4} {PO1, PO2, PO3} CO5: Explain queuing system and queuing models. {L3} {PO1, PO2, PO3} 
Unit1 
Teaching Hours:9 
UNIT I: Probability and Random Variables


Axioms of probability  Conditional probability  Total probability – Baye’s theorem Random variable  Probability mass function  Probability density function  Properties – Moments  Moment generating functions and their properties.  
Unit2 
Teaching Hours:9 
UNIT II: Standard Distributions


Poisson, Geometric, Negative Binomial, Uniform, Exponential, Gamma, Weibull and Normal distributions and their properties  Functions of a random variable.  
Unit3 
Teaching Hours:9 
UNIT III: Two Dimensional Random Variables


Joint distributions  Marginal and conditional distributions Covariance– Correlation and regression  Transformation of random variables  Central limit theorem.
 
Unit4 
Teaching Hours:9 
UNIT IV: Random Processes and Markov Chains


Classification  Stationary process  Markov process  Poisson process  Birth and death process  Markov chains  Transition probabilities  Limiting distributions. Transition Diagram.  
Unit5 
Teaching Hours:9 
UNIT V: Queuing Theory


Markovian models – M/M/1, M/M/C, finite and infinite capacity  M/M/∞ queues  Finite source model  M/G/1 queue (steady state solutions only) – Pollaczek – Khintchine formula – Special cases. Single and Multiple Server System.  
Text Books And Reference Books: T1. Ross, S., “A first course in probability”, Ninth Edition, Pearson Education, Delhi, 2013 T2. Medhi J., “Stochastic Processes”, 3^{rd} Edition,New Age Publishers, New Delhi, 2009. T3. Veerarajan, “Probability, Statistics and Random process”, Third Edition, Tata McGraw Hill, New Delhi, 2009.  
Essential Reading / Recommended Reading R1. Allen., A.O., “Probability, Statistics and Queuing Theory”, Academic press, New Delhi R2. Taha, H. A., “Operations ResearchAn Introduction”, Eighth Edition, Pearson Education Edition Asia, Delhi, 2015. R3. Gross, D. and Harris, C.M., “Fundamentals of Queuing theory”, John Wiley and Sons, Third Edition, New York, 2008.  
Evaluation Pattern · Continuous Internal Assessment (CIA): 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Subject Assignments / Online Tests : 10 marks CIA II : Mid Semester Examination (Theory) : 25 marks CIA III : Quiz/Seminar/Case Studies/Project/ Innovative Assignments/presentations/publications : 10 marks Attendance : 05 marks Total : 50 marks Mid Semester Examination (MSE) : Theory Papers:
End Semester Examination (ESE): The ESE is conducted for 100 marks of 3 hours duration. The syllabus for the theory papers are divided into FIVE units and each unit carries equal weightage in terms of marks distribution. Question paper pattern is as follows. Two full questions with either or choice will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is to test the objectiveness, analytical skill and application skill of the concept, from a question bank which reviewed and updated every year The criteria for drawing the questions from the Question Bank are as follows 50 %  Medium Level questions 25 %  Simple level questions 25 %  Complex level questions  
CS531P  COMPUTER NETWORKS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To understand the concepts of data communications. To study the functions of different layers. To introduce IEEE standards employed in computer networking. To make the students to get familiarized with different protocols and network components. To build foundation of Networks in Algorithms and its analysis, Software Engineering Models and Theory of Automata. 

Course Outcome 

CO1: Outline the basic concepts of reference models and the functionalities of physical layer in computer communications. CO2: Experiment with the data link layer protocols for error detection and corrections mechanism. CO3: Develop subnetting using IP addressing schemes and experiment with routing algorithms. CO4: Analyze the functionalities and features used in UDP and TCP protocols. CO5: Examine the Application layer protocols and cryptographic algorithms used in networking environment. 
Unit1 
Teaching Hours:9 
DATA COMMUNICATIONS


Components – Direction of Data flow – networks – Components and Categories – types of Connections – Topologies –Protocols and Standards – ISO / OSI model – Transmission Media – Coaxial Cable – Fiber Optics – Line Coding – Modems – RS232 Interfacing sequences.  
Unit2 
Teaching Hours:9 
DATA LINK LAYER


Error – detection and correction – Parity – LRC – CRC – Hamming code – low Control and Error control  stop and wait – go backN ARQ – selective repeat ARQ sliding window – HDLC.  LAN  Ethernet IEEE 802.3  IEEE 802.4  IEEE 802.5  IEEE 802.11 – FDDI  SONET – Bridges.  
Unit3 
Teaching Hours:9 
NETWORK LAYER


Internetworks – Packet Switching and Datagram approach – IP addressing methods – Subnetting – Routing – Distance Vector Routing – Link State Routing – Routers.  
Unit4 
Teaching Hours:9 
TRANSPORT LAYER


Duties of transport layer – Multiplexing – Demultiplexing – Sockets – User Datagram Protocol (UDP) – Transmission Control Protocol (TCP) – Congestion Control – Quality of services (QOS) – Integrated Services.  
Unit5 
Teaching Hours:9 
APPLICATION LAYER


Domain Name Space (DNS) – SMTP – FTP – HTTP  WWW – Security – CryptographyCase study.  
Text Books And Reference Books: T1: Behrouz A. Forouzan, “Data communication and Networking with TCP/IP protocol suite”, Tata McGrawHill, Sixth Edition, 2021. ISBN 9781264363353.  
Essential Reading / Recommended Reading R1: James F. Kurose, Keith Ross, “Computer Networking: A TopDown Approach Featuring the Internet”, Pearson Education, 2020. ISBN: 9780135928523. R2: Larry L. Peterson, Bruce S. Davie, Computer Networks: A Systems Approach Edition: 6th Edition, MKMorgan Kaufmann/Elsevier2021. ISBN: 9780128182000. R3: Andrew S. Tanenbaum, Nick Feamster, David J. Wetherall, Computer Networks: 6th Edition, Pearson, 2021, ISBN 9780136764052.  
Evaluation Pattern Continous internal assesment 70% End Semester Examination 30%  
CS532  INTRODUCTION TO ARTIFICAL INTELLIGENCE (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Course objectives: This course provides a strong foundation of fundamental concepts in Artificial Intelligence. To provide a basic exposition to the goals and methods and to enable the student to apply these techniques in applications which involve perception, reasoning and learning. 

Course Outcome 

CO1: Illustrate the basics of Artificial Intelligence and problem solving CO2: Explain the various Searching Techniques CO3: Outline the Adversial Search and CSP CO4: Make use of Knowledge Engineering in real world representation CO5: Apply the different Forms of Learning 
Unit1 
Teaching Hours:9 
INTRODUCTION


History  Applications – Components of AI  Intelligent Agents  Characteristics of Intelligent Agents  Agents and Environments  Good behavior – The nature of environments – structure of agents  Problem Solving  problem solving agents – Example problems– Searching for solutions.  
Unit2 
Teaching Hours:9 
SEARCHING TECHNIQUES


Classical Search: Uniformed Search strategies  BFS  DFS Bidirectional SearchInformed Heuristics Search Strategies Heuristic function  Greedy  best first searchA* Algorithm. local search algorithms and optimization problems –Hillclimbing Search, Simulated annealing, Local beam Search, Genetic algorithm Searching with partial observations  Online Search Agents and Unknown Environment.  
Unit3 
Teaching Hours:9 
GAME PLAYING AND CSP


Games – Optimal decisions in games –MinMax algorithm Alpha – Beta Pruning – imperfect realtime decision –Stochastic Games. Constraint Satisfaction Problem (CSP): Definition  Constraint propogation  Backtracking search  Local Search The Structure of problems.  
Unit4 
Teaching Hours:9 
KNOWLEDGE REPRESENTATION


Logic  Propositional logic  First order logic – Syntax and semantics for first order logic – Using first order logic – Knowledge engineering in first order logic. Inference in First order logic – propositional versus first order logic – unification and lifting – forward chaining – backward chaining  Resolution  Knowledge representation  Ontological Engineering  Categories and objects.  
Unit5 
Teaching Hours:9 
LEARNING


Learning from Examples : Forms of Learning  Supervised learning  Learning Decision Trees  Regression and classification with linear models, Artificial Neural Network. Knowledge in Learning : Logical formulation of learning – Explanation based learning – Learning using relevant information – Inductive logic programming. Statistical learning Learning with complete data  Learning with hidden variable.  
Text Books And Reference Books: Text Books T1. Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, 4th Edition, Pearson Education, 2020. T2. Elaine Rich; Kevin Knight; Shivashankar B Nair, “Artificial Intelligence”, 3rd Edition, Tata McGrawHill, 2019. T3. Francois Chollet “Deep Learning with Python”, 1st Edition Manning Publication, 2018.  
Essential Reading / Recommended Reading Reference Books: R1. Jeff Heaton, "Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms",1st edition, CreateSpace Independent Publishing Platform, 2013 R2. George F. Luger, " Artificial Intelligence: Structures and Strategies for Complex Problem Solving", 6th Edition, Pearson Education,2021 R3.Kevin Warwick, " Artificial Intelligence: The Basics", Routledge, 2011.  
Evaluation Pattern Continuous Internal Assessment 50% End Sem Examination 50%  
CS533P  DESIGN AND ANALYSIS OF ALGORITHMS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To introduce basic concepts of algorithms; To introduce mathematical aspects and analysis of algorithms; To introduce sorting and searching algorithms; To introduce various algorithmic techniques; To introduce algorithm design methods. 

Course Outcome 

CO1: Demonstrate the process of algorithmic problem solving with time and space complexity CO2: Identify algorithm design techniques for searching and sorting CO3: Inspect algorithms under divide and conquer technique CO4: Solve problems by applying dynamic programming technique and determine the efficiency of algorithms. CO5: Interpret the limitations of Algorithm power and demonstrate backtracking technique. 
Unit1 
Teaching Hours:9 
INTRODUCTION AND FUNDAMENTALS OF THE ANALYSIS OF ALGORITHM EFFICIENCY


Introduction, Notion of Algorithm, Fundamentals of Algorithmic Solving, Important Problem Types, Fundamentals of the Analysis Framework, Mathematical Analysis of Nonrecursive Algorithm, Mathematical Analysis of Recursive Algorithm and examples, Empirical Analysis of Algorithms and Algorithm Visualization.  
Unit2 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Brute Force and Exhaustive Search: Selection Sort, Bubble Sort, Sequential Search and Bruteforce string matching, Travelling Salesman Problem, Knapsack Problem, Assignment Problem. Decrease and Conquer: Insertion Sort and Topological Sorting and Fake Coin Problem, Russian Peasant Multiplication, Josephus Problem  
Unit3 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Divide and conquer: Merge sort, Quick Sort, Binary Tree Traversals and Related Properties and Multiplication of Large Integers and Strassen’s Matrix Multiplication. Transform and Conquer: Presorting, Notion of Heap and Heapsort, Horner’s Rule and Binary Exponentiation.  
Unit4 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Space and Time Trade  Offs: Sorting by Counting, Horspool’s and Boyer  Moore Algorithm for String Matching, Hashing. Dynamic Programming: Knapsack Problem, Warshall’s and Floyd’s Algorithm. Greedy Techniques: Prim’s Algorithm, Kruskal’s Algorithm, Dijkstra’s Algorithm.  
Unit5 
Teaching Hours:9 
ALGORITHM DESIGN TECHNIQUES


Limitations of Algorithm Power: Decision Trees, P, NP and NP Complete Problems, Challenges in Numerical Algorithms. Backtracking: nQueen’s Problem, Hamiltonian Circuit problem and SubsetSum problem. Branch and Bound: Assignment problem, Knapsack problem and Traveling salesman problem.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
CS541E01  COMPUTER GRAPHICS WITH OPEN GL (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 



Course Outcome 

CO1: Demonstrate the fundamentals of applications and techniques involved in computer graphics. CO2: Build 2D and 3D transformations using matrices representations in homogeneous coordinates. CO3: Examine OpenGL functions and relate to Crossplatform API for writing applications. CO4: Evaluate various properties of geometry. CO5: Support transformation principles, various types of light and material properties. 
Unit1 
Teaching Hours:9 
INTRODUCTION


A survey of Computer Graphics, Video Display Devices, RasterScan Systems, Graphics Workstation and Viewing Systems, Input Devices, HardCopy Devices, Graphics Networks, Graphics on the Internet.
 
Unit2 
Teaching Hours:9 
Output primitives & 2D, 3D Geometrical transforms


Line Drawing Algorithms, DDA Algorithms, Bresenham's Line Algorithm, CircleGenerating Algorithms, Midpoint Circle Algorithms, Ellipse Algorithms, Basic TwoDimensional Transformations, Matrix Representation, ThreeDimensional Translation, ThreeDimensional Rotation, ThreeDimensional Scaling, Other ThreeDimensional Transformations  Reflection and Shears.  
Unit3 
Teaching Hours:9 
Graphics in 2D with OpenGL


Java Graphics in 2D, TwoDimensional Graphics in Java, Transformations and Modeling, Basics of OpenGL and JOGL, Basic OpenGL 2D Programs, Into the Third Dimension, Drawing in 3D, Normal and Textures  
Unit4 
Teaching Hours:9 
3D viewing & Projections


Geometry, Vectors, Matrices and Homogeneous Coordinates, Primitives, Polygonal Meshes, Drawing Primitives, Viewing and Projections, Perspective Projection, Orthographic Projection, The Viewing Transform, A Simple Avatar, Viewer Nodes in Scene Graphics  
Unit5 
Teaching Hours:9 
Light, Material & Textures with Open GL


Light and Material, Vision and Color, OpenGL Materials, OpenGL Lighting, Lights and Materials in Scenes, Case Study: Textures, Texture targets, Mipmaps and Filtering, Texture Transformations, Creating Texture with OpenGL, Loading Data into Texture, Texture Coordinate Generation, Texture Objects.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
CS541E02  INTERNET AND WEB PROGRAMMING (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Explain tools for developing applications in Web programming; Describe scripting languages –Java Script; Under case study: Exposure to a web platform. 

Course Outcome 

CO1: Build the basic web page using HTML and CSS concepts. CO2: Experiment JavaScript?s for designing web applications. CO3: Make Use of react JS for building the effective web pages. CO4: Develop a back end connection using PHP. CO5: Construct web applications using platforms like Node.js 
Unit1 
Teaching Hours:9 
HTML5 and CSS3


HTML5:
Introduction to HTML5 basic tags, Forms, Multimedia (video, audio) markup and APIs, Canvas, Data Storage, Drag & Drop, Messaging & Workers CSS3: Understanding basic CSS Syntax and Styles, Understanding Display, Position, and Document Flow, Changing and styling fonts, Adding transitions and animations, Introduction to usage of bootstrap and sass.  
Unit2 
Teaching Hours:12 
Java Script


Java Script: Introduction, Java script function’s, methods and objects, Decisions and loops, Document Object, Model (DOM), JavaScript Events, Ajax and JSON, API, error handling and debugging, Filtering and Form enhancement, Introduction to Dynamic Web Programming, Implementing jQuery and JavaScript in Web Pages, Building Richly Interactive Web Pages with jQuery, Introducing jQuery UI, Getting started and building Web applications with angular JS.  
Unit3 
Teaching Hours:6 
React JS


React js: Introduction, JSX in Depth, Data Flow and Life Cycle Events, Composite and Dynamic Components and Forms, Mixins and the DOM, React on the Server, React Addons, Performance of React Apps, React Router and Data Models, Animation, React Tools, Flux, Redux and React.  
Unit4 
Teaching Hours:9 
PHP


Introduction to ServerSide Development with PHP, What is ServerSide Development, A Web Server’s Responsibilities, Quick Tour of PHP, Program Control, Functions, PHP Arrays and Super globals, Arrays, $_GET and $_POST Super global Arrays, $_SERVER Array, $_Files Array, Reading/Writing Files, PHP Classes and Objects, ObjectOriented Overview, Classes and Objects in PHP, Object Oriented Design, Error Handling and Validation, What are Errors and Exceptions?, PHP Error Reporting, PHP Error and Exception Handling, connectivity to database and processing the form.  
Unit5 
Teaching Hours:9 
CASE STUDY  Node.js


The Node Module System, The Node Programming Model, Events and Timers, The Command Line Interface, The File System, Streams, Binary Data, Executing Code, Network Programming, HTTP. Node JS Database Connectivity, MVC Framework and Architecture, Web Hosting and Content Management System, Usage of Amazon storage for web application.  
Text Books And Reference Books: TEXT BOOKS: 1. Bruce Lawson, Remy Sharp, “Introducing HTML 5”, Pearson Education, 2011. 2. Ian Lunn, “CSS3 Foundations”, Wiley Publishers, 2012. 3. Jon Duckett, “JavaScript and JQuery: Interactive FrontEnd Web Development”, Wiley Publishers: 2014. 4. Sams, “Teach Yourself AngularJS, JavaScript, and jQuery All in One”, Pearson Education ,2015. 5. Vipul A M, Prathomesh Sonpatki, “React JS by ExampleBuilding Modern Web Application with React”, Packt Publishing,2019. 6. Colin J. Ihrig, “Pro Node.js for Developers”, APRESS, 2013. 7. Randy Connolly, Ricardo Hoar, "Fundamentals of Web Development”, 1 stEdition, Pearson Education India. (ISBN:9789332575271)
 
Essential Reading / Recommended Reading REFERENCE BOOKS: 1. Matt West, “HTML5 Foundations”, Wiley Publishers: 2012. 2. Training Guide Programming in HTML5 with JavaScript and CSS3 (MCSD) (Microsoft Press Training Guide), 2013. 3. Elizabeth Castro, Bruce Hyslop, “HTML and CSS: Visual QuickStart Guide” 8^{th} edition, 2013. 4. Robin Nixon, “Learning PHP, MySQL &JavaScript with jQuery, CSS and HTML5”, 4 thEdition, O’Reilly Publications, 2015. (ISBN:9789352130153) 2) Luke Welling, Laura Thomson, “PHP and MySQL Web Development”, 5th Edition, Pearson Education, 2016. (ISBN:9789332582736) 5. Nicholas C Zakas, “Professional JavaScript for Web Developers”, 3rd Edition, Wrox/Wiley India, 2012. (ISBN:9788126535088) 4) David Sawyer Mcfarland, “JavaScript & jQuery: The Missing Manual”, 1st Edition, O’Reilly/Shroff Publishers & Distributors Pvt Ltd, 2014 (ISBN:978 9351108078) 6. Zak Ruvalcaba Anne Boehm, “Murach's HTML5 and CSS3”, 3rdEdition, Murachs/Shroff Publishers & Distributors Pvt Ltd, 2016. (ISBN:9789352133246)  
Evaluation Pattern
Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) End Semester Examination (ESE): 50% (50 marks out of 100 marks)
 
CS541E04  CRYPTOGRAPHY AND NETWORK SECURITY (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Course objectives: To understand the principles of encryption algorithms; conventional and public key cryptography. To have a detailed knowledge about authentication, hash functions and Network & applicationlevel security mechanisms. 

Course Outcome 

CO1: Explain various features of Security mechanisms and services to study Standard Block Ciphers along with their design principles. CO2: Utilize the basic concepts and algorithms of public key encryption mechanism for secure data transmission. CO3: Compare various Cryptographic authentications protocols, Hash Functions, Algorithms and Standards. CO4: Identify Various Protocols and Standards in Network Security. CO5: Make use of various research directions at system level security. 
Unit1 
Teaching Hours:9 
Introduction


OSI Security Architecture, Classical Encryption techniques, Cipher Principles, DES, Crypto analysis of DES, AES, Block Cipher Design Principles and Modes of Operation, Triple DES, Placement of Encryption Function, Traffic Confidentiality.  
Unit2 
Teaching Hours:9 
Public Key Cryptography


Introduction to Number theory, Deffie Hellman Key Exchange, Key Management, Elliptic curve Cryptography, Confidentiality using Symmetric Encryption, Public Key Cryptography and RSA.  
Unit3 
Teaching Hours:9 
Authentication & Hash Functions


Authentication Requirements, Authentication Functions, Message Authentication Codes, Hash Functions, MD5, SHA, RIPEMD and HMAC Standards  
Unit4 
Teaching Hours:9 
Network Security


Authentication Applications: Kerberos – X.509 Authentication Service – Electronic Mail Security – PGP – S/MIME  IP Security – Web Security.  
Unit5 
Teaching Hours:9 
Application Security


Intrusion detection – password management – Viruses and related Threats – Virus Counter measures – Firewall Design Principles – Trusted Systems, Secret sharing schemes, Probabilistic encryption, Quantum Encryption, Attribute and Identity Encryption CASEStudy  
Text Books And Reference Books: T1.William Stallings, “Cryptography and Network Security – Principles and Practices”, 6th Edition, 2016.  
Essential Reading / Recommended Reading R1. AtulKahate, “Cryptography and Network Security”, Tata McGrawHill, 2013. R2.Bruce Schneier, “Applied Cryptography”, John Wiley & Sons Inc, 2015. R3.Charles B. Pfleeger, Shari Lawrence Pfleeger, “Security in Computing”, Fifth Edition, Pearson Education, 2015.
 
Evaluation Pattern ASSESSMENT  ONLY FOR THEORY COURSE (without practical component) ● Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks) ● End Semester Examination(ESE) : 50% (50 marks out of 100 marks)
 
CS581  INTERNSHIP  I (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:1 
Course Objectives/Course Description 

Internships are shortterm work experiences that will allow a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities through industry exposure and practices. Course Objectives: •Identify how the internship relates to their academic courses and preferred career path •Integrate existing and new technical knowledge for industrial application •Understand lifelong learning processes through critical reflection of internship experiences. •Articulate their experience and skills to potential employers 

Course Outcome 

CO1: Design solutions to real time complex engineering problems using the concepts of Computer Science and Information Technology through independent study. CO2: Utilize acquired Skills and professional ethics for developing computational solutions. CO3: Employ the knowledge aquired to prepare a technical summary and for an oral presentation 
Unit1 
Teaching Hours:30 

Regulations


1.The student shall undergo an Internship for30 days starting from the end of 4th semester examination and completing it during the initial period of 5th semester. 2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of the students. 3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise. 4. The Internship evaluation will be completed by the end of 5th semesters. 5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance. 6. Students can also undergo internships arranged by the department during vacation. 7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors. 8. There will be an assessment for the internship for 1 credit, in the form of report assessment by the guide/mentor and a presentation on the internship given to department constituted panel.  
Text Books And Reference Books: Nil  
Essential Reading / Recommended Reading Nil  
Evaluation Pattern Maximum Marks = 50(Only credit will be displayed in the score card) Passing marks 40% min Internship assessment will be carried out based on the following parameters, during the 5th semester as a single Presentation evaluation.
 
CSHO531AIP  STATISTICAL FOUNDATION FOR ARTIFICIAL INTELLIGENCE (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:5 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

Course objectives: •Discuss the core concepts Statistical Analytics and Data manipulation •Apply the basic principles, models, and algorithms supervised and unsupervised learning mechanisms. •Analyse the structures and algorithms of regression methods •Explain notions and theories associated to Convolutional Neural Networks •Solve problems in HighDimensional Regression


Course Outcome 

•Understand and explain concepts associated to Statistical Analytics and Data manipulation L2 •Infer details of supervised and unsupervised learning mechanisms. L2 •Solve problems connected to regression methods. L3 •Analyse concepts of Convolutional Neural Networks. L4 • Appraise concepts of HighDimensional Regression. L5

Unit1 
Teaching Hours:9 
Statistical Analytics and Data manipulation


Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.  
Unit2 
Teaching Hours:9 
Techniques for supervised and unsupervised learning


The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis.  
Unit3 
Teaching Hours:9 
Neural Networks


Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, 0 Computational Considerations.  
Unit4 
Teaching Hours:9 
Random Forests and Ensemble Learning


Definition of Random Forests, Details of Random Forests Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.  
Unit5 
Teaching Hours:9 
HighDimensional Problems: p ≫ N


Diagonal Linear Discriminant Analysis and Nearest Shrunken Centroids, Linear Classifiers with Quadratic Regularization, Linear Classifiers with L1 Regularization, Classification When Features are Unavailable, HighDimensional Regression, Feature Assessment and the MultipleTesting Problem  
Text Books And Reference Books: Text Books: 1.Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2017. 2.Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.
 
Essential Reading / Recommended Reading .  
Evaluation Pattern Assessment of each paper Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) End Semester Examination (ESE): 30% (30 marks out of 100 marks) Marks CIA I: 10 Marks CIA II: 10 Marks CIA III: 10 Marks Lab: 35 Marks Total Marks: 70 Marks End Sem: 30 Marks  
CSHO531CSP  PROBABILITY AND RANDOM PROCESS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

After learning the course for a semester, the student will be aware of the important statistical information for addressing cryptography, error correction and coding, information theory and cryptanalysis. The student would also get a clear idea on some of the cases with their analytical studies in information coding and its related fields. 

Course Outcome 

CO 1: To define pattern searching algorithms for different applications CO 2: To classify vulnerability of subsystem based on the information gathered from different resources CO 3: To estimate different optimized process and models CO4: To provide means to find the similarities between the applications and vulnerabilities of the subsystem/system CO5: To analyze about best possible patterns to cluster the possible solutions for different vulnerabilities 
Unit1 
Teaching Hours:9 

Probability Fundamentals


Probability Fundamentals, Bayes’ rule, Markov chains and application to pattern search algorithms, Classical statistical inference, Bayesian statistical inference, Regression techniques  
Unit2 
Teaching Hours:9 

Information Coding


Information coding, Pseudorandom number generators, discrete random variables, special distributions and mixed random variables, link and rank analysis , probability bounds, limiting theorem and convergence  
Unit3 
Teaching Hours:9 

Statistical Learning


Risk M Basics of statistical learning: models, regression, curse of dimensionality, overfitting, etc. Optimization and convexity, Gradient descent, Newton’s method  
Unit4 
Teaching Hours:9 

Classification


Classification and similarity analysis, linear discriminative analysis, regression analysis, iterative permutation analysis, Support vector machines, nearest neighbor and application of entropy.  
Unit5 
Teaching Hours:9 

Clustering Algorithms


Clustering algorithms, graph analysis, pattern detection, Knowledge driven system design, learning with errors, Basics of neural networks  
Text Books And Reference Books: 1.Gnedenko, Boris V. Theory of probability. Routledge, 2018. 2.Beichelt, Frank. Applied Probability and Stochastic Processes. Chapman and Hall/CRC, 2016. 3.Li, X. Rong. Probability, random signals, and statistics. CRC press, 2017  
Essential Reading / Recommended Reading .  
Evaluation Pattern Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) End Semester Examination(ESE) : 30% (30 marks out of 100 marks)
 
CSHO531DAP  STATISTICAL FOUNDATION FOR DATA ANALYTICS (2020 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 

Max Marks:50 
Credits:4 

Course Objectives/Course Description 

•Discuss the core concepts Statistical Analytics and Data manipulation •Apply the basic principles, models and algorithms supervised and unsupervised learning mechanisms. •Analyse the structures and algorithms of regression methods •Analyse the use of SVM in Data Science •Explain notions and theories associated to Convolutional Neural Networks


Course Outcome 


Unit1 
Teaching Hours:9 

Statistical Analytics and Data manipulation


Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool. Statistical parameters (eg: Correlation analysis)
 
Unit2 
Teaching Hours:9 

Techniques for supervised and unsupervised learning


The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis Linear and polynomial Regression  
Unit3 
Teaching Hours:9 

Neural Networks


Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, Computational Considerations. Prediction analysis (eg: Stocks)
 
Unit4 
Teaching Hours:9 

Support Vector Machines and Flexible Discriminants


Introduction, The Support Vector Classifier, Support Vector Machines and Kernels, Generalizing Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis Time Series: predict web traffic  
Unit5 
Teaching Hours:9 

Random Forests and Ensemble Learning


Definition of Random Forests, Details of Random Forests Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.
Convolutional Neural Network  Step by Step  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading .  
Evaluation Pattern Continuous Internal Assessment (CIA) for Theory papers: 70% (70 marks out of 100 marks) End Semester Examination(ESE) : 30% (30 marks out of 100 marks) Components of the CIA CIA I :Closed Book Test and Quiz: 10 marks CIA II :Mid Semester Examination (Theory): 10 marks CIA III :Closed Book Test and Quiz:10 marks Lab marks :35 marks Attendance: 05 marks
1) CIA ASSESSMENT DETAILS  THEORY
2) LAB ASSESSMENT DETAILS
 
EC535OE01  EMBEDDED BOARDS FOR IOT APPLICATIONS (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

The aim of this course is to introduce the architecture, programming and interfacing of peripheral devices with embedded boards for IOT applications and design IOT based smart applications. 

Course Outcome 

CO1: Understand the architecture, programming and interfacing principles of ATMEGA32 AVR microcontroller and Rasberry Pi CO2: Understand the applications of ATMEGA32 AVR microcontroller, Microprocessor and Rasberry Pi in IoT CO3: Analyze the design scheme for IoT using Microcontrollers 
Unit1 
Teaching Hours:9 

NETWORKING SENSORS


Network Architecture  Sensor Network Scenarios Optimization Goals and Figures of Merit Physical Layer and Transceiver Design ConsiderationsMAC Protocols for Wireless Sensor Networks Introduction of sensors and transducers  
Unit2 
Teaching Hours:9 

ARDUINO BOARD AND its INTERFACING


ATMEGA328 microcontroller  Architecture memory organisation – Operating modes – On chip peripherals Embedded communication interfaces Example programs using Arduino IDE Integration of peripherals (Buttons & switches, digital inputs, Matrix keypad, Basic RGB colormixing, electromechanical devices Displays sensors(Temperature, Pressure, Humidity, Water level etc.), camera, real time clock, relays, actuators, Bluetooth, Wifi)  
Unit3 
Teaching Hours:9 

IoT BASED SYSTEM DESIGN


Definition of IoT Applications and Verticals System ArchitectureTypical Process FlowsTechnological Enablers Open Standard Reference Model Design Constraints and Considerations IoT Security Experiments using Arduino Platform  
Unit4 
Teaching Hours:9 

RASBERRYPI


Introduction to Raspberry pi – configuration of Raspberry pi – programming raspberry pi  Implementation of IOT with Rasberry pi  
Unit5 
Teaching Hours:9 

IMPLEMENTATION


{This unit is entirely practical based} Implementation of a IOT based real time system. The concept of the specific embedded design has to be discussed. Eg: Smart Irrigation using IOT/IoT Based Biometrics Implementation on Raspberry Pi/ Automation etc. Note: Unit – V will be based on a group project. Each group comprising of maximum 3 members. Any microcontroller can be used in UnitV  
Text Books And Reference Books: T1.Slama, Dirak “Enterprise IOT : Strategies and Best Practices for Connected Products and services”, Shroff Publisher, 1^{st} edition,2015 T2. Ali Mazidi, Sarmad Naimi, Sepehr Naimi “AVR Microcontroller and Embedded Systems: Using Assembly and C”, Pearson 2013 T3. Wentk, “Richard Raspberry Pi”, John Wiley & Sons, 2014  
Essential Reading / Recommended Reading R1. .K. Ray & K.M.Bhurchandi, “Advanced Microprocessors and peripherals Architectures, Programming and Interfacing”, Tata McGraw Hill, 2002 reprint R2. Gibson, “Microprocessor and Interfacing” Tata McGraw Hill,II edition, 2009 R3. Muhammad Ali Mazidi, Rolin D. Mckinlay, Danny Causey “8051 Microcontroller and Embedded Systems using Assembly and C” Prentice Hall of India,2008  
Evaluation Pattern
 
EC535OE02  FUNDAMENTALS OF IMAGE PROCESSING (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

The aim of this course is to introduce image processing fundamentals making the students to understand the different methods available to process an image and also give them an insight about the toolbox in MATLAB which can be used to do simulations in image processing. 

Course Outcome 

CO1: Understand the basic principles of image processing CO2: Understand the tools used for image processing applications CO3: Analyze the methods used for image preprocessing CO4: Apply the compression techniques and analyze the results CO5: Develop an image processing system for a given application 
Unit1 
Teaching Hours:9 

DIGITAL IMAGE FUNDAMENTALS


Concept of Digital Image, conversion of analog image to digital, General Applications of image processing, Fundamental Steps in Digital Image Processing. Components of an Image Processing System. Elements of Visual Perception. Light and the Electromagnetic Spectrum. Image Sensing and Acquisition. Image Sampling and Quantization  
Unit2 
Teaching Hours:9 

MATLAB USING IP TOOL BOX


Introduction to MATLAB, Introduction to IP Tool box, Exercises on image enhancement, image restoration, and image segmentation, Fourier Transform, Discrete Fourier Transform and Discrete Cosine Transform  
Unit3 
Teaching Hours:9 

IMAGE PROCESSING TECHNIQUES PART 1


Image Enhancement in the Spatial Domain: Some Basic Gray Level Transformations. Histogram Processing. Enhancement Using Arithmetic/Logic Operations. Basics of Spatial Filtering. Smoothing Spatial Filters. Sharpening Spatial Filters. Importance of Image Restoration, Model of the Image Degradation/Restoration Process. Noise Models. Filters for Image Restoration: Minimum Mean Square Error (Wiener) Filtering. Constrained Least Squares Filtering. Geometric Mean Filter  
Unit4 
Teaching Hours:9 

IMAGE PROCESSING TECHNIQUES PART 2


Image Compression: Fundamentals. Image Compression Models. Elements of Information Theory. ErrorFree Compression. Lossy Compression. Image Compression Standards. Image Segmentation: Detection of Discontinuities. Edge Linking and Boundary Detection. Thresholding. RegionBased Segmentation. Segmentation by Morphological Watersheds  
Unit5 
Teaching Hours:9 

APPLICATION OF IMAGE PROCESSING


Applications of image processing in the field of Biomedical, Remote sensing, Machine vision, Pattern recognition, and Microscopic Imaging  
Text Books And Reference Books: T1.Gonzalez and woods, Digital Image Processing using MATLAB, PHI, 2005  
Essential Reading / Recommended Reading No reference books
 
Evaluation Pattern Evaluation Pattern
 
EC535OE03  OBSERVING EARTH FROM SPACE (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

The aim of this course is to understand the basics and applications of Satellite Remote Sensing, become familiar with the usage of active and Passive remote Sensing from space and explore the applications of Satellite Remote Sensing from Ecology to National Security. The course will include some simple python based Jupyter Notebooks and opensource Remote Sensing resources. The course will introduce students to a career in Satellite remote sensing 

Course Outcome 

CO1: Understand the basics and applications of Satellite Remote Sensing CO2: Describe usage of Passive remote Sensing from space CO3: Explain the applications of active remote sensing from space CO4: Understand the applications of Satellite Remote Sensing in Agriculture, Forest Biomass Measurement, Security and Geodesy CO5: Apply the fundamentals of satellite and remote sensing for hazardoues and disaster management uses. 
Unit1 
Teaching Hours:9 

BASICS Of SATELLITES AND SATELLITE IMAGERY


History of Satellites, Types and Classification of Satellites, Launching of Satellites, orbits, attitude and orbit control, Satellite imagery and basics of Satellite datasets, Satellite Imagery for UN SDG, Satellite data analysis  
Unit2 
Teaching Hours:9 

INTRODUCTION TO PASSIVE SATELLITE IMAGERY


Concept of Imaging Spectroscopy, Difference between multispectral and hyperspectral, Spectral features, Types of Spectrometer Sensors and missions,resolution,AI and ML in satellite image analysis, Introduction to python and Jupyter notebooks for satellite image analysis  
Unit3 
Teaching Hours:9 

INTRODUCTION TO ACTIVE SATELLITE IMAGERY


Active imaging technology, radar range equation and its Implications, using amplitude phase and polarity of returned signals to measure target parameters,scattering matrix and its decomposition, Introduction to EarthEngine and Sentinel Hub  
Unit4 
Teaching Hours:9 

LAND APPLICATIONS


Use of Satellite Remote Sensing in Agriculture, Forest Biomass Measurement, Security and Geodesy  
Unit5 
Teaching Hours:9 

HAZARD AND DISASTER MANAGEMENT


Hazards and Disaster Management as per UN SDG, Use of Satellite Remote Sensing in predicting/monitoring floods, Earthquakes, volcanoes and Fires  
Text Books And Reference Books: T1. Rebekah B. Ismaili, “Earth Observation Using Python”, Wiley, 2021, Satellite Communication Anil Mainy Wiley 2010 T2. Ruiliang Pu, Hyperspectral Remote Sensing Fundamentals and Practice ,CRC Press 2017 T3. The SAR Handbook. NASA & Servir Global T4. Liguo Wong,Chunhui Zhao,Hyperspectral Image Processing,Springer 2015 T5. Matteo Pastorino and Andrea Randazzo, “ Microwave Imaging Methods and Applications”, Artech House, 2018  
Essential Reading / Recommended Reading R1. Dimitri G. Manolakis Hyperspectral Imaging Remote Sensing Physics, Sensors, and Algorithms,Cambridge University Press,2016 R2. Smith, B., Carpentier, M.H, “ The Microwave Engineering HandbookMicrowave systems and applications”, Springer  
Evaluation Pattern Evaluation Pattern
 
EE536OE01  HYBRID ELECTRIC VEHICLES (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

This course introduces the fundamental concepts, principles, analysis and design of hybrid and electric vehicles. 

Course Outcome 

CO1: To understand concepts of hybrid and electric drive configuration, types of electric machines that can be used and the energy storage devices. CO2: To recognize the application of various drive components and selection of proper component for particular applications. CO3: To demonstrate the operation on Electrical Machines used in Automotive applications and to carry out the control operation CO4: To perform mathematical modelling of the power train and to perform sizing of the components based on the design requirements. CO5: To analyse the various Energy Management strategies used in Hybrid, Electric and Conventional Vehicle with analysis on the scope of regulation of the Energy Management Control. 
Unit1 
Teaching Hours:12 
HYBRID VEHICLES


History and importance of hybrid and electric vehicles, impact of modern drivetrains on energy supplies. Basics of vehicle performance, vehicle power sources, transmission characteristics, and mathematical models to describe vehicle performance.  
Unit2 
Teaching Hours:12 
HYBRID TRACTION


Basic concept of hybrid traction, introduction to various hybrid drivetrain topologies, power flow control in hybrid drivetrain topologies, fuel efficiency analysis. Basic concepts of electric traction, introduction to various electric drivetrain topologies, power flow control in hybrid drivetrain topologies, fuel efficiency analysis.  
Unit3 
Teaching Hours:12 
MOTORS AND DRIVES


Introduction to electric components used in hybrid and electric vehicles, configuration and control of DC Motor drives, Configuration and control of Induction Motor drives, configuration and control of Permanent Magnet Motor drives, Configuration and control of Switch Reluctance Motor drives, drive system efficiency.  
Unit4 
Teaching Hours:12 
INTEGRATION OF SUBSYSTEMS


Matching the electric machine and the internal combustion engine (ICE), Sizing the propulsion motor, sizing the power electronics, selecting the energy storage technology, Communications, supporting subsystems  
Unit5 
Teaching Hours:12 
ENERGY MANAGEMENT STRATEGIES


Introduction to energy management strategies used in hybrid and electric vehicle, classification of different energy management strategies, comparison of different energy management strategies, implementation issues of energy strategies.  
Text Books And Reference Books: 1. BimalK. Bose, ‘Power Electronics and Motor drives’ , Elsevier, 2011 2. IqbalHussain, ‘Electric and Hybrid Vehicles: Design Fundamentals’, 2^{nd} edition, CRC Pr I Llc, 2010  
Essential Reading / Recommended Reading 1. Sira Ramirez, R. Silva Ortigoza, ‘Control Design Techniques in Power Electronics Devices’, Springer, 2006 2. SiewChong Tan, YukMing Lai, Chi Kong Tse, ‘Sliding mode control of switching Power Converters’, CRC Press, 2011 3. Ion Boldea and S.A Nasar, ‘Electric drives’, CRC Press, 2005  
Evaluation Pattern CIA I  20 marks CIA II midsem 50 marks CIA III  20 marks ESE  100 marks  
EE536OE02  ROBOTICS AND AUTOMATION (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

· To understand concepts in kinematics and dynamics of robotic system. · To introduce control strategies of simple robotic system. · To study the applications of computer based control to integrated automation systems. 

Course Outcome 

CO1: To understand the basic concepts in robotics. CO2: To describe basic elements in a robotic system CO3: To understand the kinematics, dynamics and programming with respect to a robotic system. CO4: To understand the control system design for a robotic system CO5: To discuss some of the robotic applications 
Unit1 
Teaching Hours:12 
Introduction


Robot definitions  Laws of robotics  Robot anatomy  History  Human systems and Robotics  Specifications of Robots  Flexible automation versus Robotic technology  Classification applications  
Unit2 
Teaching Hours:12 
Robotic systems


Basic structure of a robot – Robot end effectors  Manipulators  Classification of robots – Accuracy  Resolution and repeatability of a robot  Drives and control systems – Mechanical components of robots – Sensors and vision systems  Transducers and sensors  Tactile sensors – Proximity sensors and range sensors  Vision systems  RTOS  PLCs  Power electronics  
Unit3 
Teaching Hours:12 
Robot kinematics, dynamics and programming


Matrix representation  Forward and reverse kinematics of three degree of freedom – Robot Arm – Homogeneous transformations – Inverse kinematics of Robot – Robo Arm dynamics  DH representation of forward kinematic equations of robots  Trajectory planning and avoidance of obstacles  Path planning  Skew motion  Joint integrated motion – Straight line motion  Robot languages Computer control and Robot programming/software  
Unit4 
Teaching Hours:12 
Control system design


Open loop and feedback control  General approach to control system design  Symbols and drawings  Schematic layout  Travel step diagram, circuit and control modes  Program control  Sequence control  Cascade method  KarnaughVeitch mapping  Microcontrollers  Neural network  Artificial Intelligence  Adaptive Control – Hybrid control  
Unit5 
Teaching Hours:12 
Robot applications


Material handling  Machine loading, Assembly, inspection, processing operations and service robots  Mobile Robots  Robot cell layouts  Robot programming languages  
Text Books And Reference Books: 1. Nagrath and Mittal, “Robotics and Control”, Tata McGrawHill, 2003. 2. Spong and Vidhyasagar, “Robot Dynamics and Control”, John Wiley and sons, 2008. 3. S. R. Deb and S. Deb, ‘Robotics Technology and Flexible Automation’, Tata McGraw Hill Education Pvt. Ltd, 2010.  
Essential Reading / Recommended Reading 1. Saeed B. Niku, ‘Introduction to Robotics’,Prentice Hall of India, 2003. 2. Mikell P. Grooveret. al., "Industrial Robots  Technology, Programming and Applications", McGraw Hill, New York, 2008.  
Evaluation Pattern CIA I 20 marks CIA II  midsem 50 marks CIA III  20 marks ESE  100 marks  
EE536OE03  SMART GRIDS (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

Introducing the concepts of various components of Smart Grid, and their impacts on the energy industry, including renewable integration, PHEV penetration, demand side management, and greenhouse gas (GHG) emissions reductions. Energy policy modeling and analysis, such as policies on GHG emissions reductions and incentives to green energy investments, will be integrated into the course as well. 

Course Outcome 

CO1: Understand the concepts and principles of Smart Grid, technology enabling, and demand participation CO2: Understand the impacts of renewable resources to the grid and the various issues associated with integrating such resources to the grid. CO3: Understand the structure of an electricity market in either regulated or deregulated market conditions. CO4: Understand how (wholesale) electricity is priced in a transmission network. CO5: Evaluate the tradeoff between economics and reliability of an electric power system. CO6: Evaluate various investment options (e.g. generation capacities, transmission, renewable, demandside resources, etc) in electricity markets. 
Unit1 
Teaching Hours:9 
INTRODUCTION TO SMART GRID


Evolution of Electric Grid, Concept of Smart Grid, Definitions, Need of Smart Grid, Functions of Smart Grid, Opportunities & Barriers of Smart Grid, Difference between conventional & smart grid, Concept of Resilient &Self Healing Grid, Present development & International policies in Smart Grid. Case study of Smart Grid.CDM opportunities in Smart Grid.  
Unit2 
Teaching Hours:9 
SMART GRID TECHNOLOGIES: PART 1


Introduction to Smart Meters, Real Time Prizing, Smart Appliances, Automatic Meter Reading(AMR), Outage Management System(OMS), Plug in Hybrid Electric Vehicles(PHEV), Vehicle to Grid, Smart Sensors, Home & Building Automation, Phase Shifting Transformers.  
Unit3 
Teaching Hours:9 
SMART GRID TECHNOLOGIES: PART 2


Smart Substations, Substation Automation, FeederAutomation. Geographic Information System(GIS), Intelligent Electronic Devices(IED) & theirapplication for monitoring &protection, Smart storage like Battery, SMES, Pumped Hydro,Compressed Air Energy Storage, Wide Area Measurement System(WAMS), PhaseMeasurement Unit(PMU).  
Unit4 
Teaching Hours:9 
INFORMATION AND COMMUNICATION TECHNOLOGY FOR SMART GRID


Advanced Metering Infrastructure (AMI), Home Area Network (HAN), Neighborhood Area Network (NAN), Wide Area Network (WAN). Bluetooth, ZigBee, GPS, WiFi, WiMax based communication, Wireless Mesh Network, Basics of CLOUD Computing & Cyber Security for Smart Grid. Broadband over Power line (BPL). IP based protocols.  
Unit5 
Teaching Hours:9 
POWER QUALITY MANAGEMENT IN SMART GRID


Power Quality & EMC in Smart Grid, Power Quality issues of Grid connected Renewable Energy Sources, Power Quality Conditioners for Smart Grid, Web based Power Quality monitoring, Power Quality Audit.  
Text Books And Reference Books: 1. Ali Keyhani, Mohammad N. Marwali, Min Dai “Integration of Green and Renewable Energy in Electric Power Systems”, Wiley 2. Clark W. Gellings, “The Smart Grid: Enabling Energy Efficiency and Demand Response”,CRC Press 3. JanakaEkanayake, Nick Jenkins, KithsiriLiyanage, Jianzhong Wu, Akihiko Yokoyama,“Smart Grid: Technology and Applications”, Wiley 4. Jean Claude Sabonnadière, NouredineHadjsaïd, “Smart Grids”, Wiley Blackwell 5. Peter S. Fox Penner, “Smart Power: Climate Changes, the Smart Grid, and the Future ofElectric Utilities”, Island Press; 1 edition 8 Jun 2010 6. S. Chowdhury, S. P. Chowdhury, P. Crossley, “Microgrids and Active DistributionNetworks.” Institution of Engineering and Technology, 30 Jun 2009 7. Stuart Borlase, “Smart Grids (Power Engineering)”, CRC Press  
Essential Reading / Recommended Reading 1. Andres Carvallo, John Cooper, “The Advanced Smart Grid: Edge Power DrivingSustainability: 1”, Artech House Publishers July 2011 2. James Northcote, Green, Robert G. Wilson “Control and Automation of Electric PowerDistribution Systems (Power Engineering)”, CRC Press 3. MladenKezunovic, Mark G. Adamiak, Alexander P. Apostolov, Jeffrey George Gilbert“Substation Automation (Power Electronics and Power Systems)”, Springer 4. R. C. Dugan, Mark F. McGranghan, Surya Santoso, H. Wayne Beaty, “Electrical PowerSystem Quality”, 2nd Edition, McGraw Hill Publication 5. Yang Xiao, “Communication and Networking in Smart Grids”, CRC Press.  
Evaluation Pattern Continuous Internal Assessment (CIA) : 50% (50 marks out of 100 marks) End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Subject Assignments / Online Tests : 10 marks CIA II: Mid Semester Examination (Theory) : 25 marks CIAIII: Quiz/Seminar/Case Studies/Project/ Innovative assignments/ presentations/ publications : 10 marks Attendance : 05 marks Total : 50 marks Mid Semester Examination (MSE): Theory Papers: The MSE is conducted for 50 marks of 2 hours duration. Question paper pattern; Five out of Six questions have to be answered. Each question carries 10 marks End Semester Examination (ESE): The ESE is conducted for 100 marks of 3 hours duration. The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution. Question paper pattern is as follows. Two full questions with either or choice will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is to test the objectiveness, analytical skill and application skill of the concept, from a question bank which reviewed and updated every year The criteria for drawing the questions from the Question Bank are as follows 50 %  Medium Level questions 25 %  Simple level questions 25 %  Complex level questions  
HS521  PROJECT MANAGEMENT AND FINANCE (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

This course develops the competencies and skills for planning and controlling projects and understanding interpersonal issues that drive successful project outcomes. Focusing on the introduction of new products and processes, it examines the project management life cycle, defining project parameters, matrix management challenges, effective project management tools and techniques, and the role of a Project Manager. This course guides students through the fundamental project management tools and behavioral skills necessary to successfully launch, lead, and realize benefits from projects in profit and nonprofit organizations. 

Course Outcome 

CO1: Apply the concept of project management in engineering field through project management life cycle. CO2: Analyze the quality management and project activity in engineering field through work breakdown structure. CO3: Analyze the fundamentals of project and network diagram in engineering and management domain through PDM techniques. CO4: Understand the basics of Business finance and its applications. CO5: Understand the meaning and approached to Capital and Financial Structure 
Unit1 
Teaching Hours:9 
INTRODUCTION TO PROJECT MANAGEMENT


Introduction to Organisations, Principles of Management  its functions, Skills, Organisation Structure, Financial Feasibility. Introduction to Project, Concept, Project Management, Project Life Cycle, Role of Project Manager  Functional Areas, Qualities and Responsibilities, Impact of Delays in Project Completions.  
Unit2 
Teaching Hours:9 
PROJECT PLAN


Project management functions  Controlling, directing, project authority, responsibility, accountability, Scope of Planning, Market Analysis, Demand Forecasting, Product line analysis, Product Mix Analysis, New Product development, Plant location, plant capacity, Capital Budgeting, Time Value of Money, Cash flow importance, decision tree analysis.  
Unit3 
Teaching Hours:9 
PROJECT SCHEDULING


Introduction, Estimation of Time, Project Network Analysis  CPM and PERT model, Gantt Chart, Resource Loading,Resource Leveling, Resource Allocation. Estimating activity time and total program time, total PERT/CPM planning crash times, software‘s used in project management.  
Unit4 
Teaching Hours:9 
PROJECT MONITORING AND CONTROLLING


Introduction, Purpose, Types of control, Designing and Monitoring Systems, reporting and types. Financial Control, Quality Control, Human Resource Control, Management Control System, Project Quality Management, Managing Risks.  
Unit5 
Teaching Hours:9 
PROJECT EVALUATION AND AUDITING


Types of Project Closures, WrapUp closure activities, Purpose of Project Evaluation  Advantages, factors considered for termination of project, Project Termination process, Project Final report. Budgeting, Cost estimation, cost escalation, life cycle cost. Project finance in the roads sector, Project finance (Build Own Operate (BOO) / Build Own Operate Transfer (BOOT) Projects / Build Operate and Transfer (BOT).  
Text Books And Reference Books: Text Books: T1. “Effective Project Management”, Robert K. Wysocki, Robert Beck. Jr., and David B. Crane;  John Wiley & Sons 2003. T2. . Richard A.Brealey, Stewart C.Myers, and Mohanthy, Principles of Corporate Finance, Tata McGraw Hill, 11th Edition, 2014.  
Essential Reading / Recommended Reading Reference Books: R1. “Project Planning and Control with CPM and PERT” Dr. B.C. Punmia & K.K.Khandelwal;  Laxmi Publications, New Delhi 2011. R2. I.M.Pandey, Financial Management, Vikas Publishing House Pvt., Ltd., 11th Edition, 2008.  
Evaluation Pattern CIA  50% out of 100 ESE  50% out of 100  
IC521  INDIAN CONSTITUTION (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:0 
Course Objectives/Course Description 

This course is aimed to create awareness on the rights and responsibilities as a citizen of India and to understand the administrative structure, legal system in India. 

Course Outcome 

At the end of the course, the students will be able to: 1. Explain the fundamental rights granted to citizens of India as per the Constitution 2. Describe the Directive Principles of State Policy along with its key aspects 3. Explain the legislative powers of Union Government and its elected legislature 4. Understand the Indian judiciary with respect to civil and criminal aspects 5. Explain the working of state government and its electoral powers 
Unit1 
Teaching Hours:6 
Making of the Constitution and Fundamental Rights


Introduction to the constitution of India, the preamble of the constitution, Justice, Liberty, equality, Fraternity, basic postulates of the preamble Right to equality, Right to freedom, Right against exploitation, Right to freedom of religion, Cultural and educational rights, Right to constitutional remedies
 
Unit2 
Teaching Hours:6 
Directive Principles of State Policy and Fundamental Duties


Directive Principles of State Policy, key aspects envisaged through the directive principles, Article 51A and main duties of a citizen in India  
Unit3 
Teaching Hours:6 
Union Government and Union Legislature


the president of India, the vice president of India, election method, term, removal, executive and legislative powers, prime minister and council of ministers, election, powers, parliament, the Upper House and the Lower House, composition, function  
Unit4 
Teaching Hours:6 
Indian Judiciary


Supreme court, high courts, hierarchy, jurisdiction, civil and criminal cases, judicial activism  
Unit5 
Teaching Hours:6 
State Government and Elections in India


State executive, governor, powers , legislative council and assembly, composition, powers, electoral process, election commission, emergency  
Text Books And Reference Books: R1. B R Ambedkar, ‘The Constitution of India’. Government of India R2. Durga Das Basu, Introduction to the Constitution of India, LexisNexis, 24th edition  
Essential Reading / Recommended Reading
 
Evaluation Pattern As per university norms  
NCCOE01  NCC1 (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

· This Course is offered for cadets of NCC who have successfully completed their B Certificate. · This Course is offered for the NCC cadets in the Open Elective course offered by the department during the 5^{th} Semester. · This course can be selected if and only if the cadet Successfully Completes the ‘B’ Certificate exam that is conducted centrally oraganized by the NCC Directorate. 

Course Outcome 

Unit1 
Teaching Hours:9 
Introduction to NCC


The NCC Aims, Objectives and Org of NCCIncentivesDuties of NCC Cadet NCC Camps: Types and Conduct. National Integration Importance and Necessity Factors affecting National Integration Unity in Diversity.  
Unit2 
Teaching Hours:9 
Drill


Fundamentals of Foot Drill Word of CommandSizing Salute Basic Movements – Marching. Fundamentals of Rifle Drill  Basic Movements Introduction to .22 Rifle Handling of .22 Rifle Range procedure and Theory of grouping.  
Unit3 
Teaching Hours:9 
Social Services


Social ServicesCommunity Development  Swachh Bharat Abhiyan  Social Service Capsule Basics of Social Service Rural Development Programmes NGO’s.  
Unit4 
Teaching Hours:9 
Personality Development


Factors in personality Development SelfAwarenessEmpathy  Critical and Creative Thinking  Decision Making and Problem Solving Communication Skills Public Speaking Group Discussions.  
Unit5 
Teaching Hours:9 
Disaster Management, Health and Hygiene


Organization  Types of Disasters  Essential Services Assistance  Civil Defense Organization  Natural Disasters Man Made Disasters Firefighting Hygiene and Sanitation (Personal and Camp) First Aid in Common Medical Emergencies and Treatment of Wound.  
Text Books And Reference Books: 1.Airwing Cadet Handbook, Specialized Subject SD/SW, Maxwell Press, 2016. 2. Airwing Cadet Handbook, Common Subject SD/SW, Maxwell Press, 2015.  
Essential Reading / Recommended Reading As instructuted by commdant  
Evaluation Pattern 1. The assessment will be carried out as overall internal assessment at the end of the semester for 100 marks based on the following.
· Each cadet will appear for ‘B’ Certificate exam which is centrally conducted by the Ministry of Defense, NCC directorate. The Total marks will be for 350. · Each cadets score will be normalized to a maximum of 100 marks based on the overall marks Secured by each cadet.  
BTGE635  INTELLECTUAL PROPERTY RIGHTS (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

Innovation is crucial to us and plays significant role in the growth of economy. Government policies and legal framework offer protection to new inventions and creative works. This course intends to equip students to understand the policies and procedures they may have to rely on for the purposed of protecting their inventions or creative works during the course of their study or employment. The course consists of five units. Theories behind the protection of intellectual property and its role in promoting innovations for the progress of the society are the focus of first unit. Second unit deals with protection of inventions through patent regime in India touching upon the process of obtaining international patents. The central feature of getting patent is to establish new invention through evidence. This is done through maintaining experimental/lab records and other necessary documents. The process of creating and maintain documentary evidence is dealt in Unit 3. Computers have become an integral part of human life. Till 1980, computer related inventions were not given much importance and lying low but today they have assumed huge significance in our economy. Computer related inventions and their protection which requires special treatment under legal regimes are discussed in Unit 4. The last module deals with innovations in e commerce environment.


Course Outcome 

CO1: Understand the meaning and importance of intellectual property rights as well as different categories of intellectual property. CO2: Understand the meaning of patentable invention, the procedure for filing patent applications, rights of the patentee and the different rights of patentee. CO3: Maintain research records in the patent process, the process of patent document searching and how to interact with patent agent or attorney. CO4: Understand the issues related to patenting of software, digital rights management and database management system. CO5: Understand the intellectual property issues in e commerce, evidentiary value of electronic signature certificates, protection of websites and the protection of semiconductor integrated circuits. 
Unit1 
Teaching Hours:6 
Introduction


Detailed Syllabus: Philosophy of intellectual property  Intellectual Property & Intellectual Assists – Significance of IP for Engineers and Scientists – Types of IP – Legal framework for Protection of IP – Strategies for IP protection and role of Engineers and Scientists.  
Unit2 
Teaching Hours:6 
Patenting Inventions


Meaning of Invention – Product and Process Patents – True inventor – Applications for Patent – Procedures for obtaining Patent – Award of Patent – rights of patentee – grounds for invalidation – Legal remedies – International patents  
Unit3 
Teaching Hours:6 
Inventive Activities


Research Records in the patent process – Inventorship  Internet patent document searching and interactions with an information specialist  Interactions with a patent agent or attorney  Ancillary patent activities  Technology transfer, patent licensing and related strategies.  
Unit4 
Teaching Hours:6 
Computer Implemented Inventions


Patents and software – Business Method Patents – Data protection – Administrative methods – Digital Rights Management (DRM) – Database and Database Management systems  Billing and payment – Graphical User Interface (GUI) – Simulations – Elearning – Medical informatics – Mathematical models  
Unit5 
Teaching Hours:6 
Innovations in ECommerce


IP issues in ecommerce  Protection of websites – website hosting agreements – Copyright issues – Patentability of online business models – Jurisdiction – Digital signatures – Evidentiary value of Electronic signature certificates – Role of Certifying Authorities – Protection of Semiconductor ICs  
Text Books And Reference Books: 1. V.J. Taraporevala’s, Law of Intellectual Property, Third Edition, 2019 2. Elizabeth Verkey, Intellectual Property, Eastern Book Company, 2015  
Essential Reading / Recommended Reading 1. Martin Adelman, Cases and Materials on Patent Law, 2015 2. Avery N. Goldstein, Patent Law for Scientists and Engineers, Taylor & Francis (2005)  
Evaluation Pattern CIA 1 Assignment description: Class test to identify the different aspects of IP.
Assignment details: MCQs
CIA II (MSE) Assessment Description: Closed book exam Assignment Details: Mid semester examination five questions need to be answered.
CIA III Assessment Description: Students would be assessed on the understanding of the different forms of IP, relevant theoretical justifications of intellectual property protection and the relevant IP statute from practitioner’s approach taught in the class and their ability to apply it correctly to the given problem and proposing solutions.
Assignment details: Students will be given a hypothetical legal problem in IP and will be required to write short essay, containing maximum 500 words. In the short essay, they have to answer the following questions 1. Identify the appropriate form of intellectual property. 2. Describe whether a pertinent theoretical justification meets or does not meet the respective form of IP. 3. Apply the correct principle of IP protection to the given case. 4. Evaluate the lacunae in the existing IP mechanism in comparison to international framework. 5. Devise a correct way of handling the lacunas. ESE DETAILS  Assessment Description : Closed book exam Assignment Details: Five problem based questions need to be answered out of seven questions.  
BTGE636  INTRODUCTION TO AVIATION (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

A student successfully completing this course will be able to: Explain basic terms and concepts in air transportation, including commercial, military, and general aviation; air traffic control. Identify on the parts of an aircraft, classify the aircraft types and Construct models of an Aircraft. Understand the types of Aero engines and analyse the impact of meteorology in Aviation. 

Course Outcome 

CO1: Interpret the fundamental principles of flight based on theorems and parts of the Aircraft CO2: Summarize the types of aircrafts and illustrate modelling of an Aircraft CO3: Identify the types of Aero engines and Make use of Meteorology 
Unit1 
Teaching Hours:10 
Introduction to Principles of Flight


Development of Aviation Introduction Laws of Motion Bernoulli’s Theorem and Venturi Effect – Aero foil Forces on an Aircraft Flaps and Slats Stalling Thrust, Basic Flight Instruments Introduction of Radar Requirement of Navigation  
Unit2 
Teaching Hours:10 
Aircrafts and Aeromodelling


Airfield Layout Rules of the Air Circuit Procedure ATC / RT Procedure Aircraft Controls Fuselage – Main Tail Plane Ailerons Elevators Rudder –Landing Gear. Fighters Transports Helicopters Foreign Aircraft History of Aero modelling Materials used in Aero modelling  Types of Aero models  
Unit3 
Teaching Hours:10 
Aero Engines and Meteorology


Introduction of Aero engines  Types of EnginesPiston Engines Jet Engines – Turboprop Engines, Importance of Meteorology in Aviation Atmosphere  Clouds and Precipitation  Visibility – Humidity and Condensation  
Text Books And Reference Books: Text Books: • Airwing Cadet Handbook, Specialized Subject SD/SW, Maxwell Press, 2016. • Introduction to Aerospace Engineering: Basic Principles of Flight, Ethirajan Rathakrishnan, Wiley Press, 2021.
 
Essential Reading / Recommended Reading Reference Books: • An Observer’s Guide to Clouds and Weather, Toby Carlson, Paul Knight, and Celia Wyckoff,2015, American Meteorological Society. • Aero Engines, LNVM Society, 2007, L.N.V.M. Society Group of Institutes.  
Evaluation Pattern This Course do not have CIA 1/2/3. It has Overall CIA(out of 100 and will be Converted to 50) and ESE ( out of 100 and will be converted to 50). Total Marks=100.  
BTGE637  PROFESSIONAL PSYCHOLOGY (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

The main aim of the course is to enhance personal and professional development of the student. It will also prepare students to assume appropriate professional roles at work and develop personal awareness. Objectives of the course are


Course Outcome 


Unit1 
Teaching Hours:5 
Human Development and Growth Introduction


Psychosocial development (Erickson). Development of Cognition (Piaget), Moral Development (Kohlberg), Faith Development (Fowler) Emotional Devlopment ( Kagan)  
Unit2 
Teaching Hours:5 
SelfAwareness


Thinking Styles (Cognitive distortions), Interpersonal relationship styles (adult attachment theories), Personality styles (Jung type indicator or Myers Briggs Type Indicator), Coping styles (Emotion focused and Problem focused)  
Unit3 
Teaching Hours:5 
Social Networks and self,


Family Genogram (Bowen), Community, Genogram (Ivey), Family Dynamics (Epstein), Identifying triangles (Bowen),  
Unit4 
Teaching Hours:5 
Work Life Balance


Meaning of Work life balance and (Jim Bird) Emotion – decision link in Work life balance, Connecting life goals with work goals, improvin relationship at work, five steps to better work life balance (Jim Bird)  
Unit5 
Teaching Hours:5 
Professional development and Diversity


Coaching skills, Mentoring skills, Effective feedback, Developing a competency framework, Self Determination Theory (Ryan and Deci), Burke –Litwin change model.
 
Unit6 
Teaching Hours:5 
Diversity and challenge Cross cultural communication


Diversity and challenge Cross cultural communication, respecting diversity, Intercultural awareness, Multicultural awareness.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading Nelson Goud and Abe Arkoff, Psychology and Personal Growth, Edition, Allyn and Bacon, 2005. Richard Nelson Jones, Human Relationship skills: Coaching and self coaching, 4th edition, Routledge, 2006  
Evaluation Pattern CIA – 1 for 20 marks reduced to 10 CIA – 2 for 50 marks reduced to 25 CIA – 3 for 20 marks reduced to 10 Attendance is for 5 marks End Semester Exam for 100 marks reduced to 50
Total marks = 100  
BTGE651  DATA ANALYTICS THROUGH SPSS (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

1) COURSE OBJECTIVES
a) To make students understand the concepts used to analyse business data
b) To enable students to analyse data using softwares like SPSS
c) To enable students to understand how Analytics helps decision makers


Course Outcome 

CO1: Students will understand the concepts involved for analyzing Business data CO2: Students will be able to understand how to use software like SPSS to analyse data CO3: Students will be able to appreciate the use of Data Analytics for business decision making 
Unit1 
Teaching Hours:2 
Introduction to data Analysis


Introduction to data Analysis  
Unit2 
Teaching Hours:2 
Types of data


Different steps involved in data Analysis, Types of Data, SPSS Interface, Modules, Importing Data From excel, Creating a SPSS File  
Unit3 
Teaching Hours:4 
Types of data


Entering Differing types of Data, Defining Variables
Data Manipulation in SPSS: Recoding Variables, Splitting File, Merging Files, Weight Cases
 
Unit4 
Teaching Hours:4 
Introdcution to SPSS


Saving file and exporting results, working with output file .spv, Running Descriptive Statistics: Explore, Frequencies, Descriptive, Crosstabs, Building different types of charts  
Unit5 
Teaching Hours:4 
Univariate Analysis


Univariate Analysis: Hypothesis TestingT Test, correlation and Regression, One way and Two way ANOVA, Chi Square Test
