Department of


Syllabus for

1 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MTCS111E01  ENGLISH FOR RESEARCH PAPER WRITING  2  0  0 
MTCS112  PROFESSIONAL PRACTICE  I  2  1  0 
MTCS131  RESEARCH METHODOLOGY AND IPR  4  3  100 
MTITDA132  ADVANCED ARTIFICIAL INTELLIGENCE  4  3  100 
MTITDA133  ADVANCED DATA MINING  4  3  100 
MTITDA134  STATISTICAL FOUNDATIONS FOR DATA SCIENCE  4  3  100 
MTITDA151  ADVANCES IN DATABASE MANAGEMENT SYSTEMS LAB  4  2  50 
MTITDA152  ADVANCED DATA MINING LAB  4  2  50 
MTITIDA131  ADVANCES IN DATABASE MANAGEMENT SYSTEMS  4  3  100 
2 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MTCS212E01  CONSTITUTION OF INDIA  2  0  0 
MTCS212E02  PEDAGOGY STUDIES  2  0  0 
MTCS212E03  STRESS MANAGEMENT BY YOGA  2  0  0 
MTCS212E04  PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT SKILLS  2  0  0 
MTCS213  PROFESSIONAL PRACTICE  II  2  1  0 
MTITDA231  BIG DATA ANALYTICS  4  3  100 
MTITDA232  MACHINE LEARNING  4  3  100 
MTITDA241E01  ADVANCED DIGITAL IMAGE PROCESSING  4  3  100 
MTITDA241E02  DATA VISUALIZATION TECHNIQUES  4  3  100 
MTITDA241E03  ADVANCED SOFT COMPUTING  4  3  100 
MTITDA241E04  SOCIAL AND WEB MEDIA ANALYTICS  4  3  100 
MTITDA241E05  MASSIVE GRAPH ANALYSIS  4  3  100 
MTITDA242E01  INTERNET OF THINGS  4  3  100 
MTITDA242E02  DEEP AND REINFORCEMENT TECHNIQUES  4  3  100 
MTITDA242E04  PATTERN RECOGNITION  4  3  100 
MTITDA242E05  OPTIMIZATION TECHINIQUES  4  3  100 
MTITDA251  BIG DATA ANALYTICS LAB  4  2  50 
MTITDA252  MACHINE LEARNING LAB  4  2  50 
3 Semester  2018  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
CY01  CYBER SECURITY  2  2  50 
MTCS331E03  WEB TECHNOLOGY  4  3  100 
MTCS332E01  MACHINE LEARNING  4  3  100 
MTCS333E01  SOFTWARE PROJECT MANAGEMENT  4  3  100 
MTCS371  PROJECT WORK (PHASE I)  12  3  100 
MTCS373  INTERNSHIP  2  2  50 
4 Semester  2018  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MTCS471  PROJECT WORK (PHASEII) AND DISSERTATION  20  9  300 
 
Assesment Pattern  
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 End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks  
Examination And Assesments  
· 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)  
Department Overview:  
Department of Computer Science & Engineering started of journey to produce qualified Engineers to society with variety of skills. The department offers the degrees Bachelor of Technology, Master of Technology, and Doctor of Philosophy in the areas of Computer Science Engineering and Information Technology. The department has rich knowledge pool of faculty resource who are well trained in various fields like Artificial Intelligence, Machine learning, Computer Vision, Algorithms design, Cryptography, Computer Networking, Data mining, Data science, BIG DATA, Digital Image Processing, text mining, knowledge representation, soft computing, Cloud computing, etc.. The department has wide variety of labs setup namely open source lab , Machine learning lab , CISCO Networking Lab etc.. specifically for students for their lab curriculum and for their research.  
Mission Statement:  
VISION:
Ethical Computational Excellence
MISSION:
Imparts core and stateoftheart knowledge in the areas of Computation and Information Technology.
Promotes the culture of research and inspires innovation.
Acquaints the students with the latest industrial practices, team building and entrepreneurship.
Sensitizes the students to the environmental, social & ethical needs of society.  
Introduction to Program:  
MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (DATA ANALYTICS)
Information Technology is a 2 year, 4 semester Post graduate program aimed at studying, designing, developing, implementing, support and management of computerbased information systems. Students under this course concentrate on both software and hardware areas. It provides advanced studies in Information Systems Design, Communication Systems and Networking, Foundations of Computing Systems, and Internet and Webbased Technologies. Students have to take up electives from a wide choice of subjects, such as Embedded Low Power Systems, Object Oriented Systems, Information and System Security and Software Reliability.This course emphases on to develop the necessary skills for the students to sustain in today?s industrial expectation, in pursuit of excellence by keeping high personal and professional values and ethics.  
Program Objective:  
Program educational Objectives
? PEO1: Ability to understand ,analyze and design solutions with professional competency for the real world problems
? PEO2: Ability to develop software/embedded solutions for the requirements, based on critical analysis and research.
? PEO3: Ability to function effectively in a team and as an individual in a multidisciplinary / multicultural environment.
? PEO4: To provide a learning environment that fosters computational excellence and promote life long learning with understanding of professional responsibilities and obligations to clients and public.
Program Outcomes
1. Apply deep advanced working knowledge of computational techniques for research oriented problem solving.
2. Validating engineering models for complex Computer Science & Engineering problems through computational measures.
3. Discover knowledge, solve problems, and think about the system to meet expected needs of industry standards with appropriate considerations such as economic / environmental /societal.
4. Conduct inquiry and experimentation based on problems that have changing requirements of real world research problems.
5. Develop skills to conceive design, implement and operate systems with best of engineering practices and current trends in research.
6. To understand and adopt the impact of contextual research knowledge on social aspects and cultural issues.
7. To recognize and appreciate industry culture and importance of societal cont  
MTCS111E01  ENGLISH FOR RESEARCH PAPER WRITING (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 

 

Learning Outcome 

 
Unit1 
Teaching Hours:4 

Unit1


Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness  
Unit2 
Teaching Hours:4 

Unit2


Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction  
Unit3 
Teaching Hours:4 

Unit3


Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.  
Unit4 
Teaching Hours:4 

unit4


key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills are needed when writing an Introduction, skills needed when writing a Review of the Literature  
Unit5 
Teaching Hours:4 

Unit5


skills are needed when writing the Methods, skills needed when writing the Results, skills are needed when writing the Discussion, skills are needed when writing the Conclusions, useful phrases, how to ensure paper is as good as it could possibly be the first time submission  
Text Books And Reference Books: Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)  
Essential Reading / Recommended Reading
 
Evaluation Pattern   
MTCS112  PROFESSIONAL PRACTICE  I (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:0 
Credits:1 

Course Objectives/Course Description 

SUBJECT DESCRIPTION:
During the seminar session each student is expected to prepare and present a topic on engineering / technology, it is designed to: Review and increase their understanding of the specific topics tested. Improve their ability to communicate that understanding to the grader. Increase the effectiveness with which they use the limited examination time.
SUBJECT OBJECTIVE:
Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn.
This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines.
The course will broadly cover the following aspects: Teaching skills Laboratory skills and other professional activities Research methodology For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal.
Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline. 

Learning Outcome 

 
Unit1 
Teaching Hours:30 
na


na  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern Head of the Department will assign a suitable instructor/faculty member to each student. Students and faculty members covering a broad area will be grouped in a panel consisting of 45 students and 45 faculty members.
Within one week after registration, the student should plan the details of the topics of lectures, laboratory experiments, developmental activities and broad topic of research etc in consultation with the assigned instructor/faculty. The student has to submit two copies of the written outline of the total work to the instructor within one week.
In a particular discipline, Instructors belonging to the broad areas will form the panel and will nominate one of them as the panel coordinator. The coordinator together with the instructors will draw a complete plan of lectures to be delivered by all students in a semester. Each student will present 3 4 lectures, which will be attended by all other students and Instructors. These lectures will be evenly distributed over the entire semester. The coordinator will announce the schedule for the entire semester and fix suitable meeting time in the week.
Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography.
A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student.
The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student.
A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated.
Wherever necessary and feasible, the panel coordinator in consultation with the concerned group may also seek participation of the faculty members from other groups in lectures and comprehensive viva.
Mid semester report and final evaluation report should be submitted in the 9^{th }week and 15^{th }week of the semester respectively. These should contain the following sections:
● Section (A): Lecture notes along with two question papers each of 180 min duration, one quiz paper (CIAI) of 120 min duration on the topics of lectures. The question paper should test concepts, analytical abilities and grasp of the subject. Solutions of questions also should be provided. All these will constitute lecture material. ● Section (B): Laboratory experiments reports and professional work report.
● Section (C): Research proposal with detailed references and bibliography in a standard format.
Wherever necessary, respective Head of the Departments could be approached by Instructors/panel coordinators for smooth operation of the course. Special lectures dealing with professional ethics in the discipline may also be arranged by the group from time to time.  
MTCS131  RESEARCH METHODOLOGY AND IPR (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

The aim of the course is to introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research. 

Learning Outcome 

After going through this course the scholar will be able to 1. Explain the principles and concepts of research methodology. 2. understand the different methods of data collection 3. Apply appropriate method of data collection and analyze using statistical/software tools. 4. Present research output in a structured report as per the technical and ethical standards. 5. Create research design for a given engineering and management problem /situation. 
Unit1 
Teaching Hours:9 

Introduction to Research Methodology


Meaning, Objectives and Characteristics of research  Research methods Vs Methodology, Different Research Design: Types of research  Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process  Criteria of good research  Developing a research plan.
 
Unit2 
Teaching Hours:9 

Literature Review and Research Problem Identification


Defining the research problem  Selecting the problem  Necessity of defining the problem  Techniques involved in defining the problem  Importance of literature review in defining a problem  Survey of literature  Primary and secondary sources  Reviews, treatise, monographs, thesis reports, patents  web as a source  searching the web  Identifying gap areas from literature review  Development of working hypothesis.
 
Unit3 
Teaching Hours:9 

Data Collection & Analysis


Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external. Sampling process: Direct & Indirect Methods, Nonprobability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools.  
Unit4 
Teaching Hours:9 

Research Problem Solving


Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, nonparametric tests. Numerical problems
 
Unit5 
Teaching Hours:9 

IPR and Research Writing


IPR: Invention and Creativity Intellectual PropertyImportance and Protection of Intellectual Property Rights (IPRs) A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation  Layout, structure and language of the report – Illustrations, figures, equations and tables.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading 1. Bjorn Gustavii, “How to Write and Illustrate Scientific Papers “ Cambridge University Press, 2/e. 2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell John wiley & sons, 1/e, 2013. 3. .James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.  
Evaluation Pattern 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  
MTITDA132  ADVANCED ARTIFICIAL INTELLIGENCE (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

This course provides a strong foundation of fundamental concepts in Artificial Intelligence. To provide an empirical evidence and the scientific approach applying Artificial Intelligence techniques for problem solving using probabilistic, fuzzy, statistical and Deep Learning Models. 

Learning Outcome 


Unit1 
Teaching Hours:9 

INTRODUCTION


Intelligent Agents – Agents and environments  Good behaviour – The nature of environments – structure of agents  Problem Solving agents – Acting under uncertainty – Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited.  
Unit2 
Teaching Hours:9 

PROBABILISTIC REASONING


Representing knowledge in an uncertain domain; – The semantics of Bayesian networks; Efficient representation of conditional distributions; – Inference in Bayesian networks; Approximate inference in Bayesian Networks; – Extending probability to firstorder representations; Other approaches to Uncertain Reasoning. – Time and uncertainty; Inference in temporal models; – Hidden Markov models; – Kalman filters; Dynamic Bayesian Networks.  
Unit3 
Teaching Hours:9 

CONNECTIONIST MODELS and FUZZY LOGIC SYSTEM


Hopfield Networks; Learning in Neural Network – Application – Recurrent Network; – Distributed Representation – Fuzzy logic system Introduction. Crisp Sets– Fuzzy Sets and Terminologies – Fuzzy Logic Control – Sugeno Style of Fuzzy Inference – Fuzzy Hedges. Alpha cut Threshold – Neuro Fuzzy Systems.  
Unit4 
Teaching Hours:9 

STATISTICAL AND REINFORCEMENT


Learning from observations  forms of learning  Inductive learning  Learning decision trees  Ensemble learning  Knowledge in learning – Logical formulation of learning – Explanation based learning – Learning using relevant information – Inductive logic programming  Statistical learning methods  Learning with complete data  Learning with hidden variable  EM algorithm  Instance based learning  Reinforcement learning – Passive reinforcement learning  Active reinforcement learning  Generalization in reinforcement learning.  
Unit5 
Teaching Hours:9 

DEEP LEARNING


Convolutional Neural Networks, Motivation, Convolution operations, Pooling, Image classification, Modern CNN architectures, Recurrent Neural Network, Motivation, Vanishing/Exploding gradient problem, Applications to sequences, Modern RNN architectures, Tuning/Debugging Neural Networks, Parameter search, Overfitting, Visualizations, Pretrained Models  
Text Books And Reference Books: Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd Edition, Pearson Education, 2014. Rich and Kevin Knight, “Artificial Intelligence”, 3^{rd} Edition, Tata McGrawHill, 2012. Francois Chollet “Deep Learning with Python”, 1^{st} Edition Manning Publication, 2018  
Essential Reading / Recommended Reading Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, 1^{st} Edition, Harcourt Asia Pvt. Ltd., 2012. George F. Luger, “Artificial IntelligenceStructures and Strategies for Complex Problem Solving”, 6^{th} Edition, Pearson Education / PHI, 2009.  
Evaluation Pattern 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  
MTITDA133  ADVANCED DATA MINING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

Data mining is one of the most advanced fields of Computer Science and Engineering. This field makes use of the applications of Mathematics, Statistics and Information Technology in discovering and prediction of new information and knowledge from largely available data. It is a new evolving interdisciplinary area of research and development which has created interest among scientists of various disciplines like Computer Science, Mathematics, Statistics, and Information Technology and so on. This course titled, “Advanced Data Mining,” involves learning a collection of techniques for extracting and discovering new patterns and trends in large amounts of data. This course will also provide a handson introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management. 

Learning Outcome 


Unit1 
Teaching Hours:9 

Introduction


A multidimensional Data Model, Data preprocessing, Data cleaning, Data integration and Transformation, Correlation analysis and Data Reduction Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Data Matrix versus Dissimilarity Matrix, Proximity Measures for Nominal Attributes, Binary Attributes, Numeric Data, Ordinal Attributes,Dissimilarity for Attributes of Mixed Types.  
Unit2 
Teaching Hours:9 

Pattern Mining


Mining Frequent Patternsbasic conceptsapriori principle, Pattern Mining in Multilevel, Multidimensional Space, ConstraintBased Frequent Pattern Mining, Mining HighDimensional Data and Colossal Patterns.  
Unit3 
Teaching Hours:9 

Classification Methods


Bayesian Belief Networks, Classification by Backpropagation, Support Vector Machines, kNearestNeighbour Classifiers, Genetic Algorithms, Rough Set Approach, Fuzzy Set, Model Evaluation and Selection, Approaches, Techniques to Improve Classification Accuracy.  
Unit4 
Teaching Hours:9 

Cluster Analysis


kMeans: A CentroidBased Technique, kMedoids, Hierarchical Methods, Probabilistic ModelBased Clustering, Clustering HighDimensional Data, Clustering Graph and Network Data, Evaluation of Clustering.  
Unit5 
Teaching Hours:9 

Outlier Detection


ProximityBased Methods, and ClusteringBased Methods, Outlier Detection in HighDimensional Data. Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis.  
Text Books And Reference Books: Han J. &Kamber, M, “Data Mining: Concepts and Techniques”, Third Edition, Morgan Kaufmann, 2012.  
Essential Reading / Recommended Reading 1. PangNing Tan, Michael Steinbach, Vipin Kumar, “Introduction to Data Mining” Pearson, First Edition, 2014. 2. Mohammed J.Zaki, Wagneermeira, “Data Mining and Analysis: Fundamental concepts and algorithms”, First Edition, Cambridge University Press India, 2015. Ian H. Witten, &Eibe Frank, “Data Mining –Practical Machine Learning Tools and Techniques”, 3rd Edition, Elesvier, 2011.  
Evaluation Pattern 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  
MTITDA134  STATISTICAL FOUNDATIONS FOR DATA SCIENCE (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

ProximityBased Methods, and ClusteringBased Methods, Outlier Detection in HighDimensional Data. Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis. 

Learning Outcome 


Unit1 
Teaching Hours:9 
Discrete Random Variables and Probability


The Engineering Method andStatistical Thinking, Probability, Sample space and events, Interpretation of probability, Conditional probability, multiplication and total probability, Bayes theorem, Random variables, Discrete Random Variables, Probability Distributions and probability mass functions, Cumulative DistributionFunctions, Mean and Variance of a Discrete, Random Variable, Binomial distribution  
Unit2 
Teaching Hours:9 
Continuous Random Variables and Probability Distributions


Continuous Random Variables, Probability Distributions and Probability Density Functions, Cumulative Distribution Functions, Mean and Variance of a Continuous Random Variable, Continuous Uniform Distribution, Normal Distribution, Normal Approximation to the Binomial and Poisson Distributions, Exponential Distribution, Weibull Distribution  
Unit3 
Teaching Hours:9 
Joint Probability Distributions


Two Discrete Random Variables, Multiple Discrete Random Variables, Two Continuous Random Variables, Multiple Continuous Random Variables, Covariance and Correlation, Bivariate Normal Distribution, Linear Combinations of Random Variables,  
Unit4 
Teaching Hours:9 
Random Sampling and Data Description, Statistical Intervals for a Single Sample


Data Summary and Display, Random Sampling, StemandLeaf Diagrams, Frequency Distributions and Histograms, Box Plots, Time Sequence Plots, Probability Plots, Introduction to Statistical Intervals for a Single Sample, Confidence Interval on the Mean of a Normal Distribution, Variance Known, Confidence Interval on the Mean of a Normal Distribution, Variance Unknown, Confidence Interval on the Variance and Standard Deviation of a Normal Distribution, A LargeSample Confidence Interval for a Population Proportion, A Prediction Interval for a Future Observation  
Unit5 
Teaching Hours:9 
Tests of Hypotheses for a Single Sample, Statistical Inference for Two Samples


Hypothesis Testing, Tests on the Mean of a Normal Distribution, Variance Known, Tests on the Mean of a Normal Distribution, Variance Unknown, Tests on the Variance and Standard Deviation of a Normal Distribution, Tests on a Population Proportion, Inference For a Difference in Means of Two Normal Distributions, Variances Known, Inference For a Difference in Means of Two Normal Distributions, Variances Unknown, Inference on the Variances of Two Normal Distributions  
Text Books And Reference Books: Douglas C. Montgomery, George C. Runger, “Applied Statistics and Probability for Engineers”, Third edition, John Wiley & Sons, Inc., 2014 NinaZumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014.  
Essential Reading / Recommended Reading Rao V Dukkipati, “Probability and Statistics for Scientists and Engineers”, New Age International Publishers, First edition, 2012, Ronald E Walpole, Raymond H Myers, Sharon L Myers, Keying E Ye, “Probability and Statistics for Engineers and Scientists”, Ninth Edition, Pearson Education, 2013  
Evaluation Pattern 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  
MTITDA151  ADVANCES IN DATABASE MANAGEMENT SYSTEMS LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

 

Learning Outcome 

 
Unit1 
Teaching Hours:60 
List of Experiments


Study of all SQL commands 2. Implementation of PL/SQL Programs. 3. Implementation of Cursor, Trigger. 4. Implement the inventory control system with a reorder level 5. Develop a package for a bank to maintain its customer details 6. Develop a package for the payroll of a company 7. Implementation of IEEE/ACM paper 8. Implementation of Data Science Application Problems 9. Learning SPSS tool to implement research based concepts  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks  
MTITDA152  ADVANCED DATA MINING LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

Data mining is one of the most advanced fields of Computer Science and Engineering. This field makes use of the applications of Mathematics, Statistics and Information Technology in discovering and prediction of new information and knowledge from largely available data. It is a new evolving interdisciplinary area of research and development which has created interest among scientists of various disciplines like Computer Science, Mathematics, Statistics, and Information Technology and so on. This course titled, “Advanced Data Mining,” involves learning a collection of techniques for extracting and discovering new patterns and trends in large amounts of data. This course will also provide a handson introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management. 

Learning Outcome 

 
Unit1 
Teaching Hours:60 

List of Experiments


1. Introduction to data mining tools 2. Analysis of the various datasets by using frequent pattern mining algorithms 3. Analysis of the various datasets by using clustering algorithms 4. Analysis of the various datasets by using classifier algorithms 5. Analysis of the various datasets by using outlier detection algorithms  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks  
MTITIDA131  ADVANCES IN DATABASE MANAGEMENT SYSTEMS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

 

Learning Outcome 


Unit1 
Teaching Hours:9 
INTRODUCTION AND CONCEPTUAL MODELING


Database Management systems Application of DBMS, Advantages of DBMSER model, Components of ER diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables, Normalization  
Unit2 
Teaching Hours:9 
SQL Fundamentals


Structure Query Language, Creating Tables, Modifying Structure of table , The SELECT statement, DELETE and UPDATE, Smart table design , ALTER  
Unit3 
Teaching Hours:9 
SQL Fundamental 2


Advance Select , Multitable database design, Joins and Multitable operations, sub queries, constraints, views and transactions , security  
Unit4 
Teaching Hours:9 
DISTRIBUTED DATABASE


Distributed Databases Vs. Conventional DatabasesArchitectureFragmentationQuery ProcessingTransaction ProcessingConcurrency ControlRecovery.  
Unit5 
Teaching Hours:9 
DATA WAREHOUSING


Introduction to Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Implementation, Data Warehousing to Data Mining, KDD process.  
Text Books And Reference Books: 1. Abraham Silberschatz, Henry. F. Korth, S.Sudharsan, “Database System Concepts”, 6th Edition. Tata McGraw Hill, 2010 . Carlos Coronel & Steven Morris, “Database Systems: Design, Implementation, & Management”, February 4, 2014.  
Essential Reading / Recommended Reading PaulrajPonniah, “Data Warehousing Fundamentals”, WileyInterscience Publication, 2003.  
Evaluation Pattern 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  
MTCS212E01  CONSTITUTION OF INDIA (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 

Students will be able to: 1. Understand the premises informing the twin themes of liberty and freedom from a civil rights perspective. 2. To address the growth of Indian opinion regarding modern Indian intellectuals’ constitutional role and entitlement to civil and economic rights as well as the emergence of nationhood in the early years of Indian nationalism. 3. To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution. 

Learning Outcome 

 
Unit1 
Teaching Hours:4 
Unit1


History of Making of the Indian Constitution: History Drafting Committee, ( Composition & Working)  
Unit2 
Teaching Hours:4 
Unit2


Philosophy of the Indian Constitution: Preamble Salient Features, ∙Contours of Constitutional Rights & Duties: ∙ Fundamental Rights ∙ Right to Equality ∙ Right to Freedom ∙ Right against Exploitation ∙ Right to Freedom of Religion ∙ Cultural and Educational Rights ∙ Right to Constitutional Remedies ∙ Directive Principles of State Policy ∙ Fundamental Duties.  
Unit3 
Teaching Hours:4 
Unit 3


Organs of Governance: ∙ Parliament ∙ Composition ∙ Qualifications and Disqualifications ∙ Powers and Functions ∙ Executive ∙ President ∙ Governor ∙ Council of Ministers ∙ Judiciary, Appointment and Transfer of Judges, Qualifications ∙ Powers and Functions  
Unit4 
Teaching Hours:4 
Unit ? 4


Local Administration: ∙ District’s Administration head: Role and Importance, ∙ Municipalities: Introduction, Mayor and role of Elected Representative, CEO of Municipal Corporation. ∙Pachayati raj: Introduction, PRI: ZilaPachayat. ∙ Elected officials and their roles, CEO ZilaPachayat: Position and role. ∙ Block level: Organizational Hierarchy (Different departments), ∙ Village level: Role of Elected and Appointed officials, ∙ Importance of grass root democracy  
Unit5 
Teaching Hours:4 
Unit ? 5


Election Commission: ∙ Election Commission: Role and Functioning. ∙ Chief Election Commissioner and Election Commissioners. ∙ State Election Commission: Role and Functioning. ∙ Institute and Bodies for the welfare of SC/ST/OBC and women.  
Text Books And Reference Books: 1. The Constitution of India, 1950 (Bare Act), Government Publication. 2. Dr. S. N. Busi, Dr. B. R. Ambedkar framing of Indian Constitution, 1st Edition, 2015. 3.M. P. Jain, Indian Constitution Law, 7th Edn., Lexis Nexis, 2014.  
Essential Reading / Recommended Reading D.D. Basu, Introduction to the Constitution of India, Lexis Nexis, 2015.  
Evaluation Pattern   
MTCS212E02  PEDAGOGY STUDIES (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 

Students will be able to: 1. Review existing evidence on the review topic to inform programme design and policy making undertaken by the DfID, other agencies and researchers. 2. Identify critical evidence gaps to guide the development. 

Learning Outcome 

 
Unit1 
Teaching Hours:4 
Unit ? I


Introduction and Methodology: ∙ Aims and rationale, Policy background, Conceptual framework and terminology ∙ Theories of learning, Curriculum, Teacher education. ∙ Conceptual framework, Research questions. ∙ Overview of methodology and Searching.  
Unit2 
Teaching Hours:4 
Unit ? 2


Thematic overview: Pedagogical practices are being used by teachers in formal and informal classrooms in developing countries. ∙ Curriculum, Teacher education.  
Unit3 
Teaching Hours:4 
Unit3


Evidence on the effectiveness of pedagogical practices ∙ Methodology for the in depth stage: quality assessment of included studies. ∙ How can teacher education (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy? ∙ Theory of change. ∙ Strength and nature of the body of evidence for effective pedagogical practices. ∙ Pedagogic theory and pedagogical approaches. ∙ Teachers’ attitudes and beliefs and Pedagogic strategies.  
Unit4 
Teaching Hours:4 
unit 4


Professional development: alignment with classroom practices and followup support ∙ Peer support ∙Support from the head teacher and the community. ∙ Curriculum and assessment ∙ Barriers to learning: limited resources and large class sizes  
Unit5 
Teaching Hours:4 
Unit ? 5


Research gaps and future directions ∙ Research design ∙ Contexts ∙ Pedagogy ∙ Teacher education ∙ Curriculum and assessment ∙ Dissemination and research impact.  
Text Books And Reference Books: Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31 (2): 245261.  
Essential Reading / Recommended Reading 1. Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of Curriculum Studies, 36 (3): 361379. 2. Akyeampong K (2003) Teacher training in Ghana  does it count? Multisite teacher education research project (MUSTER) country report 1. London: DFID. 3. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of basic maths and reading in Africa: Does teacher preparation count? International Journal Educational Development, 33 (3): 272–282. 4. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education. Oxford and Boston: Blackwell. 5. Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign. www.pratham.org/images/resource%20working%20paper%202.pdf.  
Evaluation Pattern   
MTCS212E03  STRESS MANAGEMENT BY YOGA (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 

To achieve overall health of body and mind To overcome stress 

Learning Outcome 

 
Unit1 
Teaching Hours:8 
Unit1


Definitions of Eight parts of yog. ( Ashtanga )  
Unit2 
Teaching Hours:8 
unit2


Yam and Niyam. Do`s and Don’t’s in life. i) Ahinsa, satya, astheya, bramhacharya and aparigraha ii) Shaucha, santosh, tapa, swadhyay, ishwarpranidhan  
Unit3 
Teaching Hours:8 
Unit3


Asan and Pranayam i) Various yog poses and their benefits for mind & body ii)Regularization of breathing techniques and its effectsTypes of pranayam  
Text Books And Reference Books: Yogic Asanas for Group TariningPartI” :Janardan Swami YogabhyasiMandal, Nagpur  
Essential Reading / Recommended Reading “Rajayoga or conquering the Internal Nature” by Swami Vivekananda, AdvaitaAshrama (Publication Department), Kolkata  
Evaluation Pattern   
MTCS212E04  PERSONALITY DEVELOPMENT THROUGH LIFE ENLIGHTENMENT SKILLS (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:0 
Course Objectives/Course Description 



Learning Outcome 

 
Unit1 
Teaching Hours:8 
Unit1


NeetisatakamHolistic development of personality ∙ Verses 19,20,21,22 (wisdom) ∙ Verses 29,31,32 (pride & heroism) ∙ Verses 26,28,63,65 (virtue) ∙ Verses 52,53,59 (dont’s) ∙ Verses 71,73,75,78 (do’s)  
Unit2 
Teaching Hours:8 
unit2


Approach to day to day work and duties. ∙ShrimadBhagwadGeeta : Chapter 2Verses 41, 47,48, ∙ Chapter 3Verses 13, 21, 27, 35, Chapter 6Verses 5,13,17, 23, 35, ∙ Chapter 18Verses 45, 46, 48.  
Unit3 
Teaching Hours:8 
Unit3


Statements of basic knowledge. ∙ShrimadBhagwadGeeta: Chapter2Verses 56, 62, 68 ∙ Chapter 12 Verses 13, 14, 15, 16,17, 18 ∙ Personality of Role model. ShrimadBhagwadGeeta: Chapter2Verses 17, Chapter 3Verses 36,37,42, ∙ Chapter 4Verses 18, 38,39 ∙ Chapter18 – Verses 37,38,63  
Text Books And Reference Books: “Srimad Bhagavad Gita” by Swami SwarupanandaAdvaita Ashram (Publication Department), Kolkata  
Essential Reading / Recommended Reading Bhartrihari’s Three Satakam (Nitisringarvairagya) by P.Gopinath, 4. Rashtriya Sanskrit Sansthanam, New Delhi.  
Evaluation Pattern   
MTCS213  PROFESSIONAL PRACTICE  II (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:0 
Credits:1 
Course Objectives/Course Description 

Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:


Learning Outcome 

students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines. The course will broadly cover the following aspects:

Unit1 
Teaching Hours:30 

COURSE NOTICES


Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.
MAKEUPPOLICY: All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.
NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.  
Text Books And Reference Books: Selected domain related text book will be sugessted.  
Essential Reading / Recommended Reading Research papers for the selected domain  
Evaluation Pattern   
MTITDA231  BIG DATA ANALYTICS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

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 

Learning Outcome 


Unit1 
Teaching Hours:9 

UNDERSTANDING BIG DATA


What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.  
Unit2 
Teaching Hours:9 

NOSQL DATA MANAGEMENT


Introduction to NoSQL – aggregate data models – aggregates – keyvalue and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding –– version – Map reduce –partitioning and combining – composing mapreduce calculations  
Unit3 
Teaching Hours:9 

BASICS OF HADOOP


Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – filebased data structures  
Unit4 
Teaching Hours:9 

MAPREDUCE APPLICATIONS


MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Mapreduce – YARN – failures in classic Mapreduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats  
Unit5 
Teaching Hours:9 

HADOOP RELATED TOOLS


Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –cassandra examples – cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queriescase study.  
Text Books And Reference Books: 1. Tom White, "Hadoop: The Definitive Guide", 4^{th} Edition, O'Reilley, 2012. Eric Sammer, "Hadoop Operations",1^{st} Edition, O'Reilley, 2012.  
Essential Reading / Recommended Reading 1. VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013. 2. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012. 3. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011. Alan Gates, "Programming Pig", O'Reilley, 2011.  
Evaluation Pattern 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  
MTITDA232  MACHINE LEARNING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

· To introduce the basic concepts and techniques of Machine Learning. · To develop the skills in using recent machine learning software for solving practical problems. To be familiar with a set of wellknown supervised, semisupervised and unsupervised learning algorithms 

Learning Outcome 


Unit1 
Teaching Hours:9 

INTRODUCTION


Introduction overview of machine learning Different forms of learning Generative learning Gaussian parameter estimation maximum likelihood estimation MAP estimation Bayesian estimation bias and variance of estimators missing and noisy features nonparametric density estimation applications software tools.  
Unit2 
Teaching Hours:9 

CLASSIFICATION METHODS


Classification MethodsNearest neighbour Decision trees Linear Discriminant Analysis  Logistic regressionPerceptrons large margin classification Kernel methods Support Vector Machines. Classification and Regression Trees.  
Unit3 
Teaching Hours:9 

GRAPHICAL AND SEQUENTIAL MODELS


Graphical and sequential models Bayesian networks conditional independenceMarkov random fields inference in graphical models Belief propagation Markov models Hidden Markov models decoding states from observations learning HMM parameters.  
Unit4 
Teaching Hours:9 

CLUSTERING METHODS


Clustering MethodsPartitioned based Clustering  Kmeans Kmedoids; Hierarchical Clustering  Agglomerative Divisive Distance measures; Density based Clustering  DBScan; Spectral clustering.  
Unit5 
Teaching Hours:9 

NEURAL NETWORKS


Neural networks the perceptron algorithm multilayer perceptron’s back propagationnonlinear regression multiclass discrimination training procedures localized network structure dimensionality reduction interpretation.  
Text Books And Reference Books: 1. K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012. John Mueller and Luca Massaron, “Machine Learning For Dummies“, John Wiley & Sons, 2016.  
Essential Reading / Recommended Reading . T. Hastie, R. Tibshirani and J. Friedman, “Elements of Statistical Learning”, Springer, 2009. 2. E. Alpaydin, “Machine Learning”, MIT Press, 2010. 3. C. Bishop, “Pattern Recognition and Machine Learning, Springer”, 2006. 4. ShaiShalevShwartz, Shai BenDavid, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge University Press, 2014.  
Evaluation Pattern 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  
MTITDA241E01  ADVANCED DIGITAL IMAGE PROCESSING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 



Learning Outcome 

Course Outcome 1: Ability to apply the image fundamentals and mathematical transformations necessary for image processing Course Outcome 2: Ability to analyze image enhancement techniques in Spatial &frequency domain Course Outcome 3: Ability to apply restoration models and compression models for image processing Course Outcome 4: Ability to synthesis image using segmentation and representation techniques Course Outcome 5: Ability to analyze and extract potential features of interest from the image Course Outcome 6: Ability to design object recognition systems using pattern recognition techniques 
Unit1 
Teaching Hours:9 

DIGITAL IMAGE FUNDAMENTALS


Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms  
Unit2 
Teaching Hours:9 

IMAGE ENHANCEMENT & RESTORATION


Histogram modification techniques  Image smoothening  Image Sharpening  Image Restoration  Degradation Model – Noise models  Spatial filtering – Frequency domain filtering  
Unit3 
Teaching Hours:9 

IMAGE COMPRESSION & SEGMENTATION


Compression Models  Elements of information theory  Error free Compression Image segmentation –Detection of discontinuities – Region based segmentation – Morphology  
Unit4 
Teaching Hours:9 

REPRESENTATION AND DESCRIPTION


Representation schemes Boundary descriptors Regional descriptors  Relational Descriptors  
Unit5 
Teaching Hours:9 

OBJECT RECOGNITION AND INTERPRETATION


Patterns and pattern classes  DecisionTheoretic methods  Structural methodsCase studies  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading Author Name(s), “Book title”, Edition, Publisher Name, Year (if it is old edition, reprint details should be given)  
Evaluation Pattern 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  
MTITDA241E02  DATA VISUALIZATION TECHNIQUES (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

• The students will understand the basic model of the information visualization process, information processing, visual representation, interaction in visual systems and its impacts. • The students will learn the conceptual framework for visualization design. • The students will learn how information can be transformed and visualized. Helps the students to develop the skills necessary to solve visualization problems and critique and evaluate Information visualization systems. 

Learning Outcome 


Unit1 
Teaching Hours:9 

VISUALIZATION


Introduction – Issues – Data Representation – Data Presentation – Interaction.  
Unit2 
Teaching Hours:9 

FOUNDATIONS FOR DATA VISUALIZATION


Visualization stages – Experimental Semiotics based on Perception Gibson‘s Affordance theory – A Model of Perceptual Processing – Types of Data  
Unit3 
Teaching Hours:9 

COMPUTER VISUALIZATION


NonComputer Visualization – Computer Visualization: Exploring Complex Information Spaces – Fisheye Views – Applications – Comprehensible Fisheye views – Fisheye views for 3D data – Non Linear Magnification – Comparing Visualization of Information Spaces – Abstraction in computer Graphics – Abstraction in user interfaces.  
Unit4 
Teaching Hours:9 

MULTIDIMENSIONAL VISUALIZATION


One Dimension – Two Dimensions – Three Dimensions – Multiple Dimensions – Trees – Web Works – Data Mapping: Document Visualization – Workspaces  
Unit5 
Teaching Hours:9 

UNIT V


Case studySmall interactive calendars – Selecting one from many – Web browsing through a key hole – Communication analysis – Archival analysis  
Text Books And Reference Books: 1. Colin Ware, “Information Visualization Perception for Design”, 3rd edition Margon Kaufmann Publishers, 2012, (Unit II) 2. Robert Spence “Information visualization – An Introduction”, 3rd Edition, Pearson Education, 2014. (Unit I &V) 3. Stuart.K.Card, Jock.D.Mackinlay and Ben Shneiderman, “Readings in Information Visualization Using Vision to think”, Morgan Kaufmann Publishers 4. Thomas Strothotte, Computer Visualization Graphics Abstraction and Interactivity, Springer Verlag Berlin Heiderberg,  
Essential Reading / Recommended Reading   
Evaluation Pattern 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  
MTITDA241E03  ADVANCED SOFT COMPUTING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

The course help in understanding the concepts in Soft Computing techniques VIZ Fuzzy systems, Genetic algorithms, Simulated annealing, Ant Colony Optimization and Artificial Neural Networks, to apply these tools in solving problems, to analyze the strengths and weakness of these methods and to choose appropriate Soft Computing technique(s) for a given problem. 

Learning Outcome 

Explain concepts in Fuzzy sets, Fuzzy Logic, Genetic Algorithm, Simulated Annealing and Ant Colony Optimization. Illustrate how Fuzzy Logic, Simulated annealing, Genetic Algorithm and Ant Colony optimization are used to solve problems. Explain concepts in Artificial Neural Networks (MLP, RBFN, KSOM, ART, BAM, ELM, Deep NN, CNN, RNN). Illustrate the use of ANN in solving problems. Select appropriate Soft Computing technique to solve a problem. Solve Engineering problems using Soft Computing techniques. 
Unit1 
Teaching Hours:9 

FUZZY SET THEORY


Introduction to Soft Computing. Fuzzy sets and relations operations – composition. Membership functions – features – Fuzzification  membership value assignments. Defuzzification – Lambda cuts (sets and relations) – Defuzzification to scalars. Fuzzy Logic – approximate reasoning – different forms of implication. Natural language and Linguistic hedges. Fuzzy Rulebased systems – graphical techniques for inference. Extension principle and Fuzzy arithmetic. Case Studies (minimum two) – application of Fuzzy Logic.  
Unit2 
Teaching Hours:9 

OPTIMIZATION


Genetic algorithm – Biological background – Search space – Basic terminologies in GA – a simple GA – General GA – Operators in GA (Encoding, Selection, Crossover – mutation) – stopping conditions – Constraints – Problem solving  The schema theorem – advantages – applications. Case study  Application of GA. Simulated Annealing: Annealing Schedule, Parameter Selection, Applications. Case study  Application of SA. Ant Colony Optimization: Ant Foraging Behavior, artificial ants and minimum cost paths, ACO Metaheuristic, ACO algorithm for TSP problem, Theoretical considerations, convergence proof, ACO and Model based search. ACO optimization for subset problem,  
Unit3 
Teaching Hours:9 

NEURAL NETWORKS I


Supervised Learning Neural Networks – Perceptrons  Adaline – BackpropagationMutilayerPerceptrons – Radial Basis Function Networks – Unsupervised Learning Neural Networks – Competitive Learning Networks – Kohonen SelfOrganizing Networks. Case study – Application of ANN.  
Unit4 
Teaching Hours:9 

NEURAL NETWORKS II


Adaptive Resonance Theory – Introduction – ART 1 – ART2 – Applications. Basic concepts in Associative memory – BAM. Extreme Learning Machines  introduction – theory – applications case study. Hybrid soft computing systems – ANFIS – concepts and architecture  case study.  
Unit5 
Teaching Hours:9 

DEEP NETWORKS


Introduction to Deep learning – Deep neural networks – concepts. Recurrent neural network  concepts – applications. Convolutional neural network – concepts – case study based on image classification.  
Text Books And Reference Books:
3. Dorigo Marco, Stützle Thomas, “ANT COLONY OPTIMIZATION”, PHI, 2005  
Essential Reading / Recommended Reading
 
Evaluation Pattern 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  
MTITDA241E04  SOCIAL AND WEB MEDIA ANALYTICS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

This course enables the students to Understand social media, web and social media analytics, and their potential impact. Determine how to Leverage social media for better services and Understand usability metrics, web and social media metrics. 

Learning Outcome 


Unit1 
Teaching Hours:9 

Introduction to Web & Social Analytics


Overview of web & social media (Web sites, web apps, mobile apps and social media), Impact of social media on business, Social media environment, , How to leverage social media for better services, Usability, user experience, customer experience, customer sentiments, web marketing, conversion rates, ROI, brand reputation, competitive advantages Need of using analytics, Web analytics technical requirements., current analytics platforms, OpenSourcevs licensed platform, choosing right specifications & optimal solution, Web analytics and a Web analytics 2.0 framework (clickstream, multiple outcomes  
Unit2 
Teaching Hours:9 

Relevant Data And its Collection using statistical Programming language R


Data (Structured data, unstructured data, metadata, Big Data and Linked Data), Participating with people centric approach, Data analysis basics (types of data, metrics and data, descriptive statistics, comparing, Basic overview of R RData Types, RDecision Making, RLoops, Rfunctions, RStrings, Arrays, RLists, RData Frame, RCSV Files, RPie Charts, RBar charts, RBarplots. Basic Text Mining in R and word cloud.  
Unit3 
Teaching Hours:9 

KPI/Metrics


Understand the discipline of social analytics, Aligning social objectives with business goals, Identify common social business objectives, developing KPIs; Standard vs Critical metrics. PULSE metrics (Page views, Uptime, Latency, Sevenday active users) on business and technical Issues, HEART metrics (Happiness, Engagement, Adoption, Retention, and Task success) on user behaviour issues; Bounce rate, exit rate, conversion rate, engagement, Syllabus of VII & VIII Semester B.E. / Computer Science &Engg. strategically aligned KPIs, Measuring Macro & micro conversions, Onsite web analytics, offsite web analytics, the goalsignalmetric process. Case study on Readymade tools for Web and social media analytics (Key Google Analytics metrics, dashboard, social reports, Tableau Public and KNIME  
Unit4 
Teaching Hours:9 

Mining Twitter and Mining Facebook:


Why Is Twitter All the Rage?Exploring Twitter’s API, Fundamental Twitter Terminology, Creating a Twitter API Connection, Exploring Trending Topics, Searching for Tweets, Analyzing the 140 Character, Extracting Tweet Entities, Analyzing Tweets and Tweet Entities with Frequency Analysis, Computing the Lexical Diversity of Tweets, Examining Patterns in Retweets, Visualizing Frequency Data with Histograms. Analyzing Fan Pages, Examining Friendships, and More Overview, Exploring Facebook’s Social Graph API, Understanding the Social Graph API, Understanding the Open Graph Protocol, Analyzing Social Graph Connections, Analyzing Facebook Pages, Examining Friendships.  
Unit5 
Teaching Hours:9 

Data Mining in Social Media and Social Networks


Introduction, Data Mining in a Nutshell, Social Media, Motivations for Data Mining in Social Media, Data Mining Methods for Social Media, Data Representation, Data Mining  A Process, Social Networking Sites: Illustrative Examples, The Blogosphere: Illustrative Examples, Related Efforts, Ethnography and Netnography, Event Maps.Introduction, Keyword Search, Query Semantics and Answer Ranking, Keyword search over XML and relational data, Keyword search over graph data, Classification Algorithms, Clustering Algorithms, Transfer Learning in Heterogeneous Networks  
Text Books And Reference Books: 1. Matthew A. Russell, Mining of Social web, O′Reilly; 2 edition (8 October 2013), ISBN13: 9781449367619. 2. Charu C Agarwal, Social Network Data Analytics, Springer; 2011 edition (1 October 2014), 9781489988935  
Essential Reading / Recommended Reading 1. Hand, Mannila, and Smyth. Principles of Data Mining. Cambridge, MA: MIT Press, 2001. ISBN: 026208290X. 2. AvinashKaushik, Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity, John Wiley & Sons; Pap/Cdr edition (27 Oct 2009) 3. Tom Tullis, Bill Albert, Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, Morgan Kaufmann; 1 edition (28 April 2008). 4. Jim Sterne, Social Media Metrics: How to Measure and Optimize Your Marketing Investment, John Wiley & Sons (16 April 2010) Brian Clifton, Advanced Web Metrics with Google Analytics, John Wiley & Sons; 3rd Edition edition (30 Mar 2012  
Evaluation Pattern 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  
MTITDA241E05  MASSIVE GRAPH ANALYSIS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

The course presents the basic concepts in Graph analysis in the big data context. The course is intended to give exposure to basic concepts and practical aspects related to Massive Graph analysis. 

Learning Outcome 


Unit1 
Teaching Hours:9 

PregelLike Systems


Google’s Pregel –Computational model, algorithm design; Pregellike systems – Communication mechanism, Load balancing, outofcore execution, fault recovery, Ondemand querying. Case study –BigGraph@CUHK  
Unit2 
Teaching Hours:9 

Shared Memory Abstraction


Programming interfaces and its expressiveness; GraphLab; PowerLab; SinglePC diskbased systems – GraphChi, XStream, VENUS, GridGraph.  
Unit3 
Teaching Hours:9 

Block Centric Computation


Block centric vs Vertex centric; The Blogel Systems; Blogel Graph Practitioners; Block Centric API.  
Unit4 
Teaching Hours:9 

Subgraph centric Graph mining


Problem definition and existing methods; The Gthinker systems  
Unit5 
Teaching Hours:9 

Matrix based Graph Systems


Graph and Matrices; PEGUSES; GBASE; SystemML; Comparison with vertex based system  
Text Books And Reference Books: D. Yan, Y. Tian, J.Cheng, “Systems for Big Graph Analysis”, Springer, 2017  
Essential Reading / Recommended Reading 1. D. Yan, Y. Bu, Y. Tian, A. Deshpande, “Big Graph Analytics Platforms”, EBook (IEEE Explorer), 2017 R. Brath, D. Jonker, Graph Analysis and Visualization, Wiley, 2015.  
Evaluation Pattern 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  
MTITDA242E01  INTERNET OF THINGS (2019 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 basic concepts of IoT, the functionalities of different types of sensors, actuators and micro controllers. It covers the protocols used in different layers and gives insight on programming IoT for different domains. 

Learning Outcome 


Unit1 
Teaching Hours:9 

INTRODUCTION AND BACKGROUND


Definition and Characteristics of IoT, Physical Design of IoT: Things in IoT, Logical Design of IoT: IoT functional Blocks, IoT Communication Blocks, IoT communication APIs, IoT Enabling Technologies: WSN, Cloud Computing, Big Data Analysis, Communication Protocols, Embedded Systems.  
Unit2 
Teaching Hours:9 

IOT HARDWARE, DEVICES AND PLATFORMS


Basics of Arduino: The Arduino Hardware, The Arduino IDE, Basic Arduino Programming, Basics of Raspberry pi: Introduction to Raspberry Pi, Programming with Raspberry Pi, CDAC IoT devices: Ubimote, WiFi mote, BLE mote, WINGZ gateway, Introduction to IoT Platforms, IoT Sensors and actuators.  
Unit3 
Teaching Hours:9 

IOT PROTOCOLS


IoT Data Link Protocols, Network Layer Routing Protocols, Network Layer Encapsulation Protocols, Session Layer Protocols, IoT Security Protocols, Service Discovery Protocols, Infrastructure Protocols  
Unit4 
Teaching Hours:9 

IOT PROGRAMMING


Arduino Programming: Serial Communications, Getting input from sensors, Visual, Physical and Audio Outputs, Remotely Controlling External Devices, Wireless Communication. Programming with Raspberry Pi: Basics of Python Programming, Python packages of IoT, IoT Programming with CDAC IoT devices.  
Unit5 
Teaching Hours:9 

DOMAIN SPECIFIC IOT


Home automation, Smart cities, Smart Environment, IoT in Energy, Logistics, Agriculture, Industry and Health & Life style secors. Case Studies: A Case study of Internet of Things Using Wireless Sensor Networks and Smartphones, Security Analysis of InternetofThings: A Case Study of August Smart Lock, OpenIoT platform.  
Text Books And Reference Books: 1. Vijay Madisetti and ArshdeepBahga, “Internet of Things (A HandsonApproach)”, 1st Edition, VPT, 2014. 2. Margolis, Michael. “Arduino Cookbook: Recipes to Begin, Expand, and Enhance Your Projects. " O'Reilly Media, Inc.", 2011. 3. Monk, Simon. Raspberry Pi cookbook: Software and hardware problems and solutions. " O'Reilly Media, Inc.", 2016.  
Essential Reading / Recommended Reading 1. The Internet of Things: Applications to the Smart Grid and Building Automation by – Olivier Hersent, Omar Elloumi and David Boswarthick – Wiley Publications 2012. 2. Honbo Zhou, “The Internet of Things in the Cloud: A Middleware Perspective”, CRC Press, 2012. 3. David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”, Cambridge University Press, 2010. 4. AlFuqaha, Ala, et al. "Internet of things: A survey on enabling technologies, protocols, and applications." IEEE Communications Surveys & Tutorials 17.4 (2015): 23472376. 5. Tsitsigkos, Alkiviadis, et al. "A case study of internet of things using wireless sensor networks and smartphones." Proceedings of the Wireless World Research Forum (WWRF) Meeting: Technologies and Visions for a Sustainable Wireless Internet, Athens, Greece. Vol. 2325. 2012. Ye, Mengmei, et al. "Security Analysis of InternetofThings: A Case Study of August Smart Lock."  
Evaluation Pattern 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
 
MTITDA242E02  DEEP AND REINFORCEMENT TECHNIQUES (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

· To understand the fundamental principles and techniques in deep and reinforcement learning. · Helps to understand different algorithms in deep and reinforcement learning. · Helps to understand few applications of deep and reinforcement learning. To analyze few active research topics in deep and reinforcement learning areas. 

Learning Outcome 


Unit1 
Teaching Hours:9 

Introduction


Introduction Historical Trends in Deep Learning, Machine Learning Basics, History of Reinforcement Learning – Examples  Elements of Reinforcement Learning  Limitations and Scope  
Unit2 
Teaching Hours:9 

Deep Networks


Deep Feedforward NetworksExampleGradientBased LearningHidden UnitsArchitecture Design BackPropagation and Other Differentiation Algorithms, Regularization for Deep Learning, Optimization for Training Deep Models  Challenges  Basic Algorithms  Parameter Initialization  Algorithms with Adaptive Learning Rates  Approximate SecondOrder MethodsOptimization Strategies and MetaAlgorithms  
Unit3 
Teaching Hours:9 

Convolution Networks


Convolutional Networks Operation  Motivation  Pooling  Variants of the Basic Convolution Function Efficient Convolution Algorithms Random or Unsupervised Features, Sequence Modeling: Recurrent and Recursive Nets  Unfolding Computational Graphs  Recurrent Neural Networks  Bidirectional RNNs  EncoderDecoder SequencetoSequenceArchitectures Deep Recurrent Networks Recursive Neural Networks, Applications  
Unit4 
Teaching Hours:9 

Tabular Solution Methods


Multiarmed BanditsDynamic Programming  Monte Carlo Methods TemporalDifference Learning nstep Bootstrapping  
Unit5 
Teaching Hours:9 

Approximate Solution Methods


Onpolicy Prediction with Approximation Onpolicy Control with Approximation –Off policy Methods with Approximation Policy Gradient Methods  
Text Books And Reference Books: 1. Ian Goodfellow, YoshuaBengio, and Aaron Courville, “Deep Learning” MIT Press, 2016. Richard S. Sutton and Andrew G. Barto,“Reinforcement Learning: An Introduction” second edition, MIT Press.  
Essential Reading / Recommended Reading 1. CosmaRohillaShalizi, Advanced Data Analysis from an Elementary Point of View, 2015. Deng & Yu, Deep Learning: Methods and Applications, Now Publishers, 2013.)  
Evaluation Pattern 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  
MTITDA242E04  PATTERN RECOGNITION (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

Objective of this course is to enable to students to learn the fundamentals of Pattern Recognition techniques, Statistical Pattern recognition techniques, Syntactical Pattern recognition techniques and Neural Pattern recognition techniques useful for computer vision applications. 

Learning Outcome 


Unit1 
Teaching Hours:9 

PATTERN RECOGNITION OVERVIEW


Pattern recognition, Classification and Description—Patterns and feature Extraction with Examples—Training and Learning in PR systems—Pattern recognition Approaches  
Unit2 
Teaching Hours:9 

STATISTICAL PATTERN RECOGNITION


Introduction to statistical Pattern Recognition—supervised Learning using Parametric and Non Parametric Approaches  
Unit3 
Teaching Hours:9 

LINEAR DISCRIMINANT FUNCTIONS &UNSUPERVISED LEARNING AND CLUSTERING


IntroductionDiscrete and binary Classification problems—Techniques to directly Obtain linear Classifiers  Formulation of Unsupervised Learning Problems—Clustering for unsupervised learning and classification  
Unit4 
Teaching Hours:9 

SYNTACTIC PATTERN RECOGNITION


Overview of Syntactic Pattern Recognition—Syntactic recognition via parsing and other grammars–Graphical Approaches to syntactic pattern recognition—Learning via grammatical inference  
Unit5 
Teaching Hours:9 

NEURAL PATTERN RECOGNITION


Introduction to Neural networks—Feedforward Networks and training by Back Propagation—Case Study  Content Addressable Memory Approaches and Unsupervised Learning in Neural PR  
Text Books And Reference Books: Robert Schalkoff, “Pattern Recognition: Statistical Structural and Neural Approaches”, John Wiley & sons, Inc, 2012(Reprint edition).  
Essential Reading / Recommended Reading 1. Earl Gose, Richard Johnsonbaugh, Steve Jost, “Pattern Recognition and Image Analysis”, Prentice Hall of India,.Pvt Ltd, New Delhi, 2011. 2. Bishop C.M., “Neural Networks for Pattern Recognition”, Oxford University Press, 2005. 3. Duda R.O., P.E.Hart & D.G Stork, “Pattern Classification”, 2nd Edition, J.WileyInc 2001. Duda R.O. & Hart P.E., “Pattern Classification and Scene Analysis”, J.WileyInc, 1973.  
Evaluation Pattern 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  
MTITDA242E05  OPTIMIZATION TECHINIQUES (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:3 

Course Objectives/Course Description 

Introduction to optimization techniques using both linear and nonlinear programming. The focus of the course is on convex optimization though some techniques will be covered for nonconvex function optimization too. After an adequate introduction to linear algebra and probability theory, students will learn to frame engineering minima maxima problems in the framework of optimization problems. 

Learning Outcome 


Unit1 
Teaching Hours:9 

Mathematical preliminaries


Linear algebra and matrices Vector space, eigen analysis, Elements of probability theory, Elementary multivariable calculus  
Unit2 
Teaching Hours:9 

Linear Programming


Simplex method, Introduction to linear programming model, Duality, Karmarkar's method  
Unit3 
Teaching Hours:9 

Unconstrained optimization


Conjugate direction and quasiNewton methods, Gradientbased methods, Onedimensional search methods  
Unit4 
Teaching Hours:9 

Constrained Optimization


Lagrange theorem FONC, SONC, and SOSC conditions  
Unit5 
Teaching Hours:9 

Nonlinear problems


Projection methods, KKT conditions, Nonlinear constrained optimization models  
Text Books And Reference Books: Introduction To Optimization 4Th Edition by Edwin K. P. Chong & Stanislaw H. Zak, Wiley India, 2017.  
Essential Reading / Recommended Reading Nonlinear Programming, 3^{rd} edition by Dimitri Bertsekas, Athena Scientific, 2016  
Evaluation Pattern 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  
MTITDA251  BIG DATA ANALYTICS LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 



Learning Outcome 

 
Unit1 
Teaching Hours:60 

List of projects


 
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern For subjects having practical as part of the subject End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks  
MTITDA252  MACHINE LEARNING LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 



Learning Outcome 

To implement Machine Learning Algorithms 
Unit1 
Teaching Hours:60 

List of Experiments


 
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern For subjects having practical as part of the subject End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks  
CY01  CYBER SECURITY (2018 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

Cyber Security is defined as the body of technologies, processes and practices designed to protect networks, computers, programs and data from attack, damage or unauthorized access. Similar to other forms of security, Cyber Security requires coordinated effort throughout an information system. This course will provide a comprehensive overview of the different facets of Cyber Security. In addition, the course will detail into specifics of Cyber Security for all parties who may be involved keeping view of Global and Indian Legal environment. 

Learning Outcome 

After learning the course for a semester, the student will be aware of the important cyber laws in the Information Technology Act (ITA) 2000 and ITA 2008 with knowledge in the areas of Cyberattacks and Cybercrimes happening in and around the world. The student would also get a clear idea on some of the cases with their analytical studies in Hacking and its related fields. 
Unit1 
Teaching Hours:6 
UnitI


Security Fundamentals, Social Media and Cyber Security Security Fundamentals  Social Media –IT Act CNCI – Legalities  
Unit2 
Teaching Hours:6 
UnitII


Cyber Attack and Cyber Services Vulnerabilities  Phishing  Online Attacks. – Cyber Attacks  Cyber Threats  Denial of Service Vulnerabilities  Server Hardening  
Unit3 
Teaching Hours:6 
UnitIII


Risk Management and Assessment  Risk Management Process  Threat Determination Process  Risk Assessment  Risk Management Lifecycle – Vulnerabilities, Security Policy Management  Security Policies  Coverage Matrix, Business Continuity Planning  Disaster Types  Disaster Recovery Plan  Business Continuity Planning  Business Continuity Planning Process.  
Unit4 
Teaching Hours:6 
UnitIV


Vulnerability  Assessment and Tools: Vulnerability Testing  Penetration Testing Architectural Integration: Security Zones  Devices viz Routers, Firewalls, DMZ Host, Extenuating Circumstances viz. BusinesstoBusiness, Exceptions to Policy, Special Services and Protocols, Configuration Management  Certification and Accreditation  
Unit5 
Teaching Hours:6 
UnitV


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 Analysis, Cyber Evolution: Cyber Organization  Cyber Future  
Text Books And Reference Books:
TEXT BOOKS:
REFERENCES:
 
Essential Reading / Recommended Reading Research papers from reputed journals.  
Evaluation Pattern Internal 50 Marks.  
MTCS331E03  WEB TECHNOLOGY (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

This course is designed to introduce programming experience to techniques associated with the World Wide Web. The course will introduce webbased mediarich programming tools for creating interactive web pages. Basic animation programming is also introduced with an emphasis on mediarich content creation, distribution and tracking capabilities. 

Learning Outcome 

· Analyze Build web applications using PHP, JSP and Servlets and client side script technologies like HTML, CSS and JavaScript with Apache web server.
· Design and Integrate database environment to web applications being developed. Describe sessions conceptually and implement using cookies and URL.
· Analyze the XML applications with DTD and style sheets that span multiple domains and across various platforms.
· Analyze the reasons and effects of nonstandard clientside scripting language characteristics, such as limited data types, dynamic variable types and properties, and extensive use of automatic type conversion.

Unit1 
Teaching Hours:9 
INTRODUCTION


Introduction – Network concepts – Web concepts – Internet addresses  Retrieving Data with URL – HTML – DHTML: Cascading Style Sheets  Scripting Languages: JavaScript.
 
Unit2 
Teaching Hours:9 
COMMON GATEWAY INTERFACE


Common Gateway Interface: Programming CGI Scripts – HTML Forms – Custom Database Query Scripts – Server Side Includes – Server security issues  
Unit3 
Teaching Hours:9 
XML AND RICH INTERNET APPLICATIONS


XML XSL, XSLT, DOM ,RSS, Client Technologies Adobe Flash, Flex, Microsoft Silverlight.  
Unit4 
Teaching Hours:9 
SERVER SIDE PROGRAMMINGI


Server side Programming – PHP Passing variables between pages, Using tables, Form elements. Active server pages – Java server pages  
Unit5 
Teaching Hours:9 
SERVER SIDE PROGRAMMINGII & APPLICATIONS


Java Servlets: Servlet container – Exceptions – Sessions and Session Tracking – Using Servlet context – Dynamic Content Generation – Servlet Chaining and Communications. Simple applications – Internet Commerce – Database connectivity.  
Text Books And Reference Books: 1. Deitel, Deitel and Neito, “INTERNET and WORLD WIDE WEB – How to program”, Pearson education asia, 4^{th} Edition , 2011 2. Beginning PHP, Apache, MySql Web Development , Timothy, Elizabath, Jason, Wrox ,2012  
Essential Reading / Recommended Reading 1. Eric Ladd and Jim O’Donnell, et al, “USING HTML 4, XML, and JAVA1.2”, PHI publications, 2003. 2. Jeffy Dwight, Michael Erwin and Robert Nikes “USING CGI”, PHI Publications, 1999  
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) 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  
MTCS332E01  MACHINE LEARNING (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 



Learning Outcome 

Upon Completion of the course, the students will be able to

Unit1 
Teaching Hours:9 
INTRODUCTION


Machine Learning  Machine Learning Foundations –Overview – applications  Types of machine learning  basic concepts in machine learning Examples of Machine Learning Applications – Linear Models for Regression  Linear Basis Function Models  The BiasVariance Decomposition  Bayesian Linear Regression  Bayesian Model Comparison  
Unit2 
Teaching Hours:9 
SUPERVISED LEARNING


Linear Models for Classification  Discriminant Functions Probabilistic Generative Models  Probabilistic Discriminative Models  Bayesian Logistic Regression. Decision Trees – Classification Trees Regression Trees  Pruning. Neural Networks Feedforward Network Functions  Error Backpropagation  Regularization  Mixture Density and Bayesian Neural Networks  Kernel Methods  Dual Representations  Radial Basis Function Networks. Ensemble methods Bagging Boosting.  
Unit3 
Teaching Hours:9 
UNSUPERVISED LEARNING


Clustering Kmeans  EM  Mixtures of Gaussians  The EM Algorithm in General Model selection for latent variable models  highdimensional spaces  The Curse of Dimensionality –Dimensionality Reduction  Factor analysis  Principal Component Analysis  Probabilistic PCA Independent components analysis  
Unit4 
Teaching Hours:9 
PROBABILISTIC GRAPHICAL MODELS


Directed Graphical Models  Bayesian Networks  Exploiting Independence Properties – From Distributions to Graphs Examples Markov Random Fields  Inference in Graphical Models – Learning –Naive Bayes classifiersMarkov Models – Hidden Markov Models – Inference – Learning Generalization – Undirected graphical models Markov random fields Conditional independence properties  Parameterization of MRFs  Examples  Learning  Conditional random fields (CRFs)  Structural SVMs  
Unit5 
Teaching Hours:9 
ADVANCED LEARNING


Sampling – Basic sampling methods – Monte Carlo. Reinforcement Learning  KArmed Bandit  Elements  ModelBased Learning  Value Iteration  Policy Iteration. Temporal Difference Learning Exploration Strategies Deterministic and Nondeterministic Rewards and Actions Eligibility Traces Generalization Partially Observable States The Setting Example. Semi  Supervised Learning. Computational Learning Theory  Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading 1. Tom Mitchell, "Machine Learning", McGrawHill, 1997.  
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) 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  
MTCS333E01  SOFTWARE PROJECT MANAGEMENT (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 



Learning Outcome 

Explain and practice the process of project management and its application in delivering successful IT projects. Evaluate a project to develop the scope of work, provide accurate cost estimates and to plan the various activities. Interpret and use risk management analysis techniques that identify the factors that put a project at risk and to quantify the likely effect of risk on project timescales. Identify the resources required for a project and to produce a work plan and resource Schedule. Monitor and evaluate the progress of a project and to assess the risk of slippage, revising targets or counteract drift. Distinguish between the different types of project and follow the stages needed to negotiate an appropriate contract. 
Unit1 
Teaching Hours:9 
Project Evaluation and Project Planning


Importance of Software Project Management, Activities Methodologies, Categorization of Software Projects , Setting objectives , Management Principles, Management Control, Project portfolio Management, Costbenefit evaluation technology, Risk evaluation, Strategic program Management, Stepwise Project Planning.
 
Unit2 
Teaching Hours:9 
Project Life Cycle and Effort


Software process and Process Models, Choice of Process models, mental delivery, Rapid Application development, Agile methods, Extreme Programming, SCRUM, Managing interactive processes, Basics of Software estimation, Effort and Cost estimation techniques, COSMIC Full function points, COCOMO II A Parametric Productivity Model, Staffing Pattern.  
Unit3 
Teaching Hours:9 
Activity Planning and Risk Management


Objectives of Activity planning, Project schedules, Activities, Sequencing and scheduling, Network Planning models, Forward Pass & Backward Pass techniques, Critical path (CRM) method, Risk identification, Assessment, Monitoring, PERT technique, Monte Carlo simulation, Resource Allocation, Creation of critical patterns, Cost schedules.
 
Unit4 
Teaching Hours:9 
Project Management and Control


Framework for Management and control, Collection of data Project termination, Visualizing progress, Cost monitoring, Earned Value AnalysisProject tracking, Change controlSoftware Configuration Management, Managing contracts, Contract Management.  
Unit5 
Teaching Hours:9 
Staffing In Software Projects


Managing people, Organizational behaviour, Best methods of staff selection, Motivation, The Oldham  Hackman job characteristic model, Ethical and Programmed concerns, Working in teams, Decision making, Team structures, Virtual teams, Communications genres , Communication plans.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
3. Software Project Management in Practice by Pankaj Jalote, Pearson Education 2010. 4. Software Project Management Readings and Cases by Chris Kemerer 2010.
 
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) 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  
MTCS371  PROJECT WORK (PHASE I) (2018 Batch)  
Total Teaching Hours for Semester:180 
No of Lecture Hours/Week:12 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

During this seminar session, each student is expected to prepare and present a topic on engineering/ technology, itis designed to:


Learning Outcome 

students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines. The course will broadly cover the following aspects:

Unit1 
Teaching Hours:45 

UNIT1


 
Text Books And Reference Books: Selected domain related text book will be sugessted.  
Essential Reading / Recommended Reading Research papers for the selected domain  
Evaluation Pattern § Continuous Internal Assessment:100 Marks ¨ Presentation assessed by Panel Members ¨ Guide ¨ Mid semester Project Report  
MTCS373  INTERNSHIP (2018 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

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 trough industry exposure and practices. More specifically, doing internships is beneficial because they provide the opportunity to:


Learning Outcome 


Unit1 
Teaching Hours:60 
Regulations


1.The student shall undergo an Internship for30 days starting from the end of 2nd semester examination and completing it during the initial period of 3rd 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 shall be completed by the end of 2nd 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 2 credits, 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: Related to the Internship domain text books are sugessted.  
Essential Reading / Recommended Reading Readings Related to the Internship domain  
Evaluation Pattern Internal 50 Marks  
MTCS471  PROJECT WORK (PHASEII) AND DISSERTATION (2018 Batch)  
Total Teaching Hours for Semester:300 
No of Lecture Hours/Week:20 
Max Marks:300 
Credits:9 
Course Objectives/Course Description 

Objective of this course is to encourage students to do research oriented project. 

Learning Outcome 

The students are expected to comeout with the product implemetation with dissertation details. 
Unit1 
Teaching Hours:120 
Assessment of Project Work(Phase II) and Dissertation


v § Continuous Internal Assessment:100 Marks ¨ Presentation assessed by Panel Members ¨ Assessment by Guide § Dissertation (Exclusive assessment of Project Report): 100 Marks § End Semester Examination:100 Marks ¨ Viva Voce ¨ Demonstration ¨ Project Report  
Text Books And Reference Books: Research articles from the identified domain  
Essential Reading / Recommended Reading Research papers from reputed journals  
Evaluation Pattern Internal 200 External 100 