Department of
ELECTRONICS-AND-COMMUNICATION-ENGINEERING






Syllabus for
Master of Technology (Information Technology)
Academic Year  (2019)

 
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 (PHASE-II) 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 state-of-the-art 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 computer-based 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 Web-based 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

-

Unit-1
Teaching Hours:4
Unit-1
 

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

Unit-2
Teaching Hours:4
Unit-2
 

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

Unit-3
Teaching Hours:4
Unit-3
 

Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

Unit-4
Teaching Hours:4
unit-4
 

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

Unit-5
Teaching Hours:4
Unit-5
 

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

Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press

Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM. Highman’sbook.

Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht Heidelberg London, 2011

 

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

-

Unit-1
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 4-5 students and 4-5 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 9th week and 15th 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 (CIA-I) 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.

Unit-1
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.

 

Unit-2
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.

 

Unit-3
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, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools.

Unit-4
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, non-parametric tests. Numerical problems

 

Unit-5
Teaching Hours:9
IPR and Research Writing
 

IPR: Invention and Creativity- Intellectual Property-Importance 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:
  1.     Kothari C.R., “Research Methodology Methods and techniques”, New Age International, New Delhi, 2004.
  2.    Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.
  3.    Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992.
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

·         Demonstrate the concepts and features of agents, environments and uniformed search strategies.

Understand inference using Bayesian Networks, Hidden Markov Models as an approach to Probabilistic Reasoning

Experiment the Fuzzy Logic Systems to Neural Network Architectures

Compare and contrast performance of different Statistical learning methods used in machine learning

Explore Deep Learning models to image and text processing application

Unit-1
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.

Unit-2
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 first-order representations; Other approaches to Uncertain Reasoning. – Time and uncertainty; Inference in temporal models; – Hidden Markov models; – Kalman filters; Dynamic Bayesian Networks.

Unit-3
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.

Unit-4
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.

Unit-5
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”, 3rd Edition, Tata McGraw-Hill, 2012.

       Francois Chollet “Deep Learning with Python”, 1st Edition Manning Publication, 2018

Essential Reading / Recommended Reading

Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, 1st Edition, Harcourt Asia Pvt. Ltd., 2012.

       George F. Luger, “Artificial Intelligence-Structures and Strategies for Complex Problem Solving”, 6th 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 hands-on introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management.

Learning Outcome

1.      Explain the fundamental issues involved in the use of the training/test methodology, cross-validation and the bootstrap to provide accuracy assessments.

2.      Demonstrate accurate and efficient use of classification and related data mining techniques, using Python Programming for the computations.

3.      Demonstrate capacity for mathematical reasoning through analyzing, proving and explaining concepts from the theory that underpins clustering and related data mining methods.

4.      Understand and explain ideas of source and target sample, and their relevance to the practical application relevance to the society of proximity based and clustering methods and other data mining techniques.

5.      Design data mining solutions to analyze real-world data sets.

Unit-1
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.

Unit-2
Teaching Hours:9
Pattern Mining
 

Mining Frequent Patterns-basic concepts-apriori principle, Pattern Mining in Multilevel, Multidimensional Space, Constraint-Based Frequent Pattern Mining, Mining High-Dimensional Data and Colossal Patterns.

Unit-3
Teaching Hours:9
Classification Methods
 

Bayesian Belief Networks, Classification by Backpropagation, Support Vector Machines, kNearest-Neighbour Classifiers, Genetic Algorithms, Rough Set Approach, Fuzzy Set, Model Evaluation and Selection, Approaches, Techniques to Improve Classification Accuracy.

Unit-4
Teaching Hours:9
Cluster Analysis
 

k-Means: A Centroid-Based Technique, k-Medoids, Hierarchical Methods, Probabilistic Model-Based Clustering, Clustering High-Dimensional Data, Clustering Graph and Network Data, Evaluation of Clustering.

Unit-5
Teaching Hours:9
Outlier Detection
 

Proximity-Based Methods, and Clustering-Based 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.      Pang-Ning 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

 

Proximity-Based Methods, and Clustering-Based Methods, Outlier Detection in HighDimensional Data.

Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis.

Learning Outcome

Demonstrate the concepts of discrete random variables and probability

Illustrate the concepts of continuous random variables

Conduct the experiment on joint probability distribution

Discuss the fundamental concepts of statistical intervals

Describe the basic concepts of single sample and two samples in statistical methods

Unit-1
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

Unit-2
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

Unit-3
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, 

Unit-4
Teaching Hours:9
Random Sampling and Data Description, Statistical Intervals for a Single Sample
 

Data Summary and Display, Random Sampling, Stem-and-Leaf 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 Large-Sample Confidence Interval for a Population Proportion,  A Prediction Interval for a Future Observation

Unit-5
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

-

Unit-1
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 hands-on introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management.

Learning Outcome

-

Unit-1
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

Explain the fundamentals of Database systems

Illustrate the basic concepts of SQL

Examine the advanced concepts of SQL

Examine the concepts of distributed database

Demonstrate the fundamentals of data warehousing

Unit-1
Teaching Hours:9
INTRODUCTION AND CONCEPTUAL MODELING
 

Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables, Normalization

Unit-2
Teaching Hours:9
SQL Fundamentals
 

Structure Query Language, Creating Tables, Modifying Structure of table , The SELECT statement, DELETE and UPDATE, Smart table design , ALTER 

Unit-3
Teaching Hours:9
SQL Fundamental -2
 

Advance Select , Multi-table database design, Joins and Multi-table operations, sub queries, constraints, views and transactions , security

Unit-4
Teaching Hours:9
DISTRIBUTED DATABASE
 

Distributed Databases Vs. Conventional Databases-Architecture-Fragmentation-Query Processing-Transaction Processing-Concurrency Control-Recovery.

Unit-5
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”, Wiley-Interscience 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

-

Unit-1
Teaching Hours:4
Unit-1
 

History of Making of the Indian Constitution: History Drafting Committee, ( Composition & Working)

Unit-2
Teaching Hours:4
Unit-2
 

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.

Unit-3
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

Unit-4
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

Unit-5
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

-

Unit-1
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.

Unit-2
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.

Unit-3
Teaching Hours:4
Unit-3
 

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.

Unit-4
Teaching Hours:4
unit 4
 

Professional development: alignment with classroom practices and follow-up support Peer support Support from the head teacher and the community. Curriculum and assessment Barriers to learning: limited resources and large class sizes

Unit-5
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): 245-261.

Essential Reading / Recommended Reading

1.      Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of Curriculum Studies, 36 (3): 361-379.

2.      Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site 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

-

Unit-1
Teaching Hours:8
Unit-1
 

Definitions of Eight parts of yog. ( Ashtanga )

Unit-2
Teaching Hours:8
unit-2
 

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

Unit-3
Teaching Hours:8
Unit-3
 

Asan and Pranayam i) Various yog poses and their benefits for mind & body ii)Regularization of breathing techniques and its effects-Types of pranayam

Text Books And Reference Books:

Yogic Asanas for Group Tarining-Part-I” :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

 
  • To learn to achieve the highest goal happily
  • To become a person with stable mind, pleasing personality and determination
  • To awaken wisdom in students

Learning Outcome

-

Unit-1
Teaching Hours:8
Unit-1
 

Neetisatakam-Holistic 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)

Unit-2
Teaching Hours:8
unit-2
 

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

Unit-3
Teaching Hours:8
Unit-3
 

Statements of basic knowledge. ShrimadBhagwadGeeta: Chapter2-Verses 56, 62, 68 Chapter 12 -Verses 13, 14, 15, 16,17, 18 Personality of Role model. ShrimadBhagwadGeeta: Chapter2-Verses 17, Chapter 3-Verses 36,37,42, Chapter 4-Verses 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 (Niti-sringar-vairagya) 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:

  • Review and increasetheir understandingof thespecific topics tested.
  • Improvetheir abilityto communicate that understandingto thegrader.
  • Increasetheeffectiveness with which theyusethelimited examinationtime.

 

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:

  • Teachingskills
  • Laboratoryskills andother professional activities
  • Research methodology

Unit-1
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 map-reduce analytics using Hadoop and related tools

Learning Outcome

CO 1: Describe big data and use cases from selected business domains

CO 2: Discuss open source technologies

CO 3: Explain NoSQL big data management

CO 4: Discuss basics of Hadoop and HDFS

CO 5: Discuss map-reduce analytics using Hadoop

CO 6: Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data Analytics

Unit-1
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.

Unit-2
Teaching Hours:9
NOSQL DATA MANAGEMENT
 

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding –– version – Map reduce –partitioning and combining – composing map-reduce calculations

Unit-3
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 – file-based data structures

Unit-4
Teaching Hours:9
MAPREDUCE APPLICATIONS
 

MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats

Unit-5
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 queries-case study.

Text Books And Reference Books:

1.      Tom White, "Hadoop: The Definitive Guide", 4th  Edition, O'Reilley, 2012.

Eric Sammer, "Hadoop Operations",1st 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 well-known supervised, semi-supervised and unsupervised learning algorithms

Learning Outcome

Select real-world applications that needs machine learning based solutions.

Implement and apply machine learning algorithms.

Select appropriate algorithms for solving a particular group of real-world problems.

Recognize the characteristics of machine learning techniques that are useful to solve real-world problems.

Unit-1
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. 

Unit-2
Teaching Hours:9
CLASSIFICATION METHODS
 

Classification Methods-Nearest neighbour- Decision trees- Linear Discriminant Analysis - Logistic regression-Perceptrons- large margin classification- Kernel methods- Support Vector Machines. Classification and Regression Trees. 

Unit-3
Teaching Hours:9
GRAPHICAL AND SEQUENTIAL MODELS
 

Graphical and sequential models- Bayesian networks- conditional independence-Markov random fields- inference in graphical models- Belief propagation- Markov models- Hidden Markov models- decoding states from observations- learning HMM parameters.

Unit-4
Teaching Hours:9
CLUSTERING METHODS
 

Clustering Methods-Partitioned based Clustering - K-means- K-medoids; Hierarchical Clustering - Agglomerative- Divisive- Distance measures; Density based Clustering - DBScan; Spectral clustering. 

Unit-5
Teaching Hours:9
NEURAL NETWORKS
 

Neural networks- the perceptron algorithm- multilayer perceptron’s- back propagation-nonlinear 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. ShaiShalev-Shwartz, Shai Ben-David, “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

 

The students will learn the fundamental concepts of Image Processing.

The students will learn image enhancement techniques in spatial & frequency domain

The students will learn the restoration & compression models.

Help the students to segmentation and representation techniques for the region of interests.

The students will learn the how to recognize objects using pattern recognition techniques

 

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

Unit-1
Teaching Hours:9
DIGITAL IMAGE FUNDAMENTALS
 

Image formation, Image transforms – Fourier transforms, Walsh, Hadamard, Discrete cosine, Hotelling transforms

Unit-2
Teaching Hours:9
IMAGE ENHANCEMENT & RESTORATION
 

Histogram modification techniques - Image smoothening - Image Sharpening - Image Restoration - Degradation Model – Noise models - Spatial filtering – Frequency domain filtering

Unit-3
Teaching Hours:9
IMAGE COMPRESSION & SEGMENTATION
 

Compression Models - Elements of information theory - Error free Compression -Image segmentation –Detection of discontinuities – Region based segmentation – Morphology

Unit-4
Teaching Hours:9
REPRESENTATION AND DESCRIPTION
 

Representation schemes- Boundary descriptors- Regional descriptors - Relational Descriptors

Unit-5
Teaching Hours:9
OBJECT RECOGNITION AND INTERPRETATION
 

Patterns and pattern classes - Decision-Theoretic methods - Structural methods-Case studies

Text Books And Reference Books:

1.      Gonzalez.R.C& Woods. R.E., “Digital Image Processing”, 3rd Edition, Pearson Education, Indian edition published by Dorling Kindersely India Pvt. Ltd. Copyright © 2009, Third impression 2011.

2.      Gonzalez.R.C& Woods. R.E., “Digital Image Processing using MATLAB”, 2nd Edition, McGraw Hill Education (India) Pvt Ltd  2011 (Asia)

      Madan, “ An Introduction to MATLAB for Behavioural Researchers”, Sage Publications, 2014

 

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

Ability to explain and interpret the basic model of the information visualization and interactive systems.

Ability to investigate and experiment analysis the different Visualization techniques and mapping the data to visual representations

Ability to understand and implement Fisheye visualizations in the information space.

Ability to measure different types of information that can be transformed into visualization.

Ability to solve and organize the visualization problems and to demonstrate visualization based user interface.

Unit-1
Teaching Hours:9
VISUALIZATION
 

Introduction – Issues – Data Representation – Data Presentation – Interaction.

Unit-2
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

Unit-3
Teaching Hours:9
COMPUTER VISUALIZATION
 

Non-Computer 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.

Unit-4
Teaching Hours:9
MULTIDIMENSIONAL VISUALIZATION
 

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

Unit-5
Teaching Hours:9
UNIT V
 

Case study-Small 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.

Unit-1
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 Rule-based systems – graphical techniques for inference. Extension principle and Fuzzy arithmetic.

Case Studies (minimum two) – application of Fuzzy Logic.

Unit-2
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,  

Unit-3
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 Self-Organizing Networks.  

Case study – Application of ANN.

Unit-4
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.

Unit-5
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:
  1. Sivanandam&Deepa,  “Principles of Soft Computing”, 2nd Edition, Wiley India, 2011
  2. T. J. Ross,  “Fuzzy Logic with Engineering Applications”, 3rd Edition, Wilev, 2014

3. Dorigo Marco, Stützle Thomas, “ANT COLONY OPTIMIZATION”, PHI, 2005

Essential Reading / Recommended Reading
  1. Rajasekaran and G A V Pai, “ Neural Networks, Fuzzy Logic and Genetic Algorithm”, 1stEdn, PHI, 2011
  2. D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, 1stEdn, Pearson, 2016
  3.  J S R Jang, C T Sun and E Mizutani, “ Neuro-Fuzzy and Soft Computing”, 1stEdn, Pearson, 2015
  4. Charu C. Agrawal, “Neural Networks and Deep Learning”, Springer, 2018
  5. Frank Millstein, “Convolutional Neural Networks in Python”, CreateSpace Independent Publishing Platform, 2018
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

Use Social Media Analytics and Web analytics

Explain how to leverage social media for better services

Develop KPIs and to build scorecards & dashboards to track Key Performance Indicators (KPIs).

Understand text mining and data mining in social networks.

 Use ready-made web analytics tools (Google Analytics) and be able to understand a statistical programming language (R), also use its graphical development environment (Deduce) for data exploration and analysis

Unit-1
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

Unit-2
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 R-Data Types, R-Decision Making, R-Loops, R-functions, R-Strings, Arrays, R-Lists, R-Data Frame, R-CSV Files, R-Pie Charts, R-Bar charts, R-Barplots. Basic Text Mining in R and word cloud.

Unit-3
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, Seven-day 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, On-site web analytics, off-site web analytics, the goal-signal-metric process. Case study on Ready-made tools for Web and social media analytics (Key Google Analytics metrics, dashboard, social reports, Tableau Public and KNIME

Unit-4
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.

Unit-5
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), ISBN-13: 978-1449367619.

2.   Charu C Agarwal, Social Network Data Analytics, Springer; 2011 edition (1 October 2014), 978-1489988935

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

Identify applications of massive graph analysis algorithms.

Examine basic concepts in Pregel-like systems

Evaluate Graph Analytic tools

Compare block centric and vertex centric systems

Explain Sub-graph centric and matrix based graph systems

Unit-1
Teaching Hours:9
Pregel-Like Systems
 

Google’s Pregel –Computational model, algorithm design; Pregel-like systems –  Communication mechanism, Load balancing, out-of-core execution, fault recovery, On-demand querying.

Case study –BigGraph@CUHK

Unit-2
Teaching Hours:9
Shared Memory Abstraction
 

Programming interfaces and its expressiveness; GraphLab; PowerLab; Single-PC disk-based systems – GraphChi, X-Stream, VENUS, GridGraph.

Unit-3
Teaching Hours:9
Block Centric Computation
 

Block centric vs Vertex centric; The Blogel Systems; Blogel Graph Practitioners;  Block Centric API.

Unit-4
Teaching Hours:9
Sub-graph centric Graph mining
 

Problem definition and existing methods; The G-thinker systems

Unit-5
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”, E-Book (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

Explain the fundamental building blocks of an IoT environment from a logical and physical perspective.

Experiment with Arduino and Raspberry Pi to choose the appropriate hardware for different IoT projects.

Summarize various IoT protocols in Application and Network layers by outlining their advantages and disadvantages.

Develop IoT solutions using Arduino and Raspberry Pi to solve real life problems.

Survey successful IoT products and solutions to analyze their architecture and technologies.

Unit-1
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.

Unit-2
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, Wi-Fi mote, BLE mote, WINGZ gateway, Introduction to IoT Platforms, IoT Sensors and actuators.

Unit-3
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

Unit-4
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.

Unit-5
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 Internet-of-Things: A Case Study of August Smart Lock, OpenIoT platform.

Text Books And Reference Books:

1. Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands-on-Approach)”, 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.      Al-Fuqaha, Ala, et al. "Internet of things: A survey on enabling technologies, protocols, and applications." IEEE Communications Surveys & Tutorials 17.4 (2015): 2347-2376.

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 Internet-of-Things: 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

Ability to explain and describe the basics of deep learning and reinforcement techniques

Ability to investigate different regularization and optimization techniques for training deep neural networks.

Ability to implement convolution and recurrent neural networks

Ability to implement and compare various iteration, Monte Carlotemporal-difference reinforcement learning algorithms

Ability to construct and apply on-policy and off-policy reinforcement learning algorithms with function approximation

Unit-1
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

Unit-2
Teaching Hours:9
Deep Networks
 

Deep Feedforward Networks-Example-Gradient-Based Learning-Hidden Units-Architecture Design-  Back-Propagation and Other Differentiation Algorithms, Regularization for Deep Learning, Optimization for Training Deep Models - Challenges - Basic Algorithms - Parameter Initialization - Algorithms with Adaptive Learning Rates - Approximate Second-Order Methods-Optimization Strategies and Meta-Algorithms

Unit-3
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 - Encoder-Decoder Sequence-to-SequenceArchitectures -Deep Recurrent Networks -Recursive Neural Networks,

Applications

Unit-4
Teaching Hours:9
Tabular Solution Methods
 

Multi-armed Bandits-Dynamic Programming - Monte Carlo Methods -Temporal-Difference Learning -n-step Bootstrapping 

Unit-5
Teaching Hours:9
Approximate Solution Methods
 

On-policy Prediction with Approximation -On-policy 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

Course Outcome 1: Ability to understand the basic concepts of Pattern Recognition and its approaches

Course Outcome 2: Ability to Illustrate various statistical methods for supervised learning classification

Course Outcome 3: Ability to Evaluate the Clustering for Unsupervised learning classification

Course Outcome 4: Ability to apply various syntactic pattern classification methods.

Course Outcome 5: Ability to analyse neural networks and Pattern Recognition methods

Unit-1
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

Unit-2
Teaching Hours:9
STATISTICAL PATTERN RECOGNITION
 

Introduction   to statistical   Pattern Recognition—supervised   Learning   using Parametric and Non Parametric Approaches

Unit-3
Teaching Hours:9
LINEAR DISCRIMINANT FUNCTIONS &UNSUPERVISED LEARNING AND CLUSTERING
 

Introduction-Discrete   and   binary   Classification   problems—Techniques   to directly   Obtain   linear   Classifiers   - Formulation   of   Unsupervised   Learning Problems—Clustering for unsupervised learning and classification

Unit-4
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

Unit-5
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 non-linear programming. The focus of the course is on convex optimization though some techniques will be covered for non-convex 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

Demonstrate the concepts of fundamental concepts of optimization techniques

Illustrate the concepts of Linear programming

Conduct the experiment on constraint based optimization 

Discuss the fundamental concepts of constraint based optimization 

Describe the basic concepts of non linear problems

Unit-1
Teaching Hours:9
Mathematical preliminaries
 

Linear algebra and matrices  Vector space, eigen analysis,  Elements of probability theory,  Elementary multivariable calculus

Unit-2
Teaching Hours:9
Linear Programming
 

Simplex method, Introduction to linear programming model,  Duality,  Karmarkar's method

Unit-3
Teaching Hours:9
Unconstrained optimization
 

Conjugate direction and quasi-Newton methods, Gradient-based methods, One-dimensional search methods

Unit-4
Teaching Hours:9
Constrained Optimization
 

Lagrange theorem  FONC, SONC, and SOSC conditions

Unit-5
Teaching Hours:9
Non-linear problems
 

Projection methods, KKT conditions, Non-linear 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, 3rd 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

 

To provide a strong foundation of fundamental concepts of Big Data Analytics

To enable the student to apply data analytics using advanced tools such as Hadoop,cassandra, Hbase and Hive.

 

Learning Outcome

-

Unit-1
Teaching Hours:60
List of projects
 

List of Experiments

Project based Lab : List of the project titles are given below

  1. Implementation of aggregate data model uisng No SQL
  2. Implementation of file system for performing data analytics uisngHadoop/Cassandra
  3. Implementation of data model and clients uingHbase
  4. Application development using Hive
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

 

To introduce basic machine learning techniques.To develop the skills in using recent machine learning software for solving practical problems in high-performance computing environment.To develop the skills in applying appropriate supervised, semi-supervised or unsupervised learning algorithms for solving practical problems.

 

Learning Outcome

   To implement Machine Learning Algorithms

Unit-1
Teaching Hours:60
List of Experiments
 

.    List of Experiments

  1.       Exercises to solve the real-world problems using the following machine learning methods:
  •  Linear Regression
  •  Logistic Regression
  •  Multi-Class Classification
  •  Neural Networks
  •  Support Vector Machines
  •  K-Means Clustering & PCA

 

  1.       Develop programs to implement Anomaly Detection & Recommendation Systems.
  2.       Implement GPU computing models to solving some of the problems mentioned in Problem 1.
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 Cyber-attacks and Cyber-crimes 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.

Unit-1
Teaching Hours:6
Unit-I
 

Security Fundamentals, Social Media and Cyber Security Security Fundamentals - Social Media –IT Act- CNCI – Legalities

Unit-2
Teaching Hours:6
Unit-II
 

Cyber Attack and Cyber Services Vulnerabilities - Phishing - Online Attacks. – Cyber Attacks - Cyber Threats - Denial of Service Vulnerabilities  - Server Hardening  

Unit-3
Teaching Hours:6
Unit-III
 

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.

Unit-4
Teaching Hours:6
Unit-IV
 

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Architectural Integration: Security Zones - Devices viz Routers, Firewalls, DMZ Host, Extenuating Circumstances viz. Business-to-Business, Exceptions to Policy, Special Services and Protocols, Configuration Management - Certification and Accreditation

Unit-5
Teaching Hours:6
Unit-V
 

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:   

  1. Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, Cyber Security Policy Guidebook, Wiley; 1 edition , 2012,  ISBN-10: 1118027809 
  2. Dan Shoemaker and Wm. Arthur Conklin, Cybersecurity: The Essential Body Of Knowledge,   Delmar Cengage Learning; 1 edition (May 17, 2011) ,ISBN-10: 1435481690
  3. Jason Andress, The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice, Syngress; 1 edition (June 24, 2011) ,  ISBN-10: 1597496537
  1. Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 3rd Edition 2002. 
  2. Bruce, Schneier, “Applied Cryptography”, 2nd Edition, Toha Wiley & Sons, 2007. 
  3. Man Young Rhee, “Internet Security”, Wiley, 2003. 
  4. Pfleeger & Pfleeger, “Security in Computing”, Pearson Education, 3rd Edition, 2003.  

 

REFERENCES:

  1. Information Technology Act 2008 Online 2. IT Act 2000.

 

 

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 web-based media-rich programming tools for creating interactive web pages. Basic animation programming is also introduced with an emphasis on media-rich 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 client-side scripting language characteristics, such as limited data types, dynamic variable types and properties, and extensive use of automatic type conversion.

 

 

Unit-1
Teaching Hours:9
INTRODUCTION
 

Introduction – Network concepts – Web concepts – Internet addresses - Retrieving Data with URL – HTML – DHTML: Cascading Style Sheets - Scripting Languages: JavaScript.

 

Unit-2
Teaching Hours:9
COMMON GATEWAY INTERFACE
 

Common Gateway Interface: Programming CGI Scripts – HTML Forms – Custom Database Query Scripts – Server Side Includes – Server security issues 

Unit-3
Teaching Hours:9
XML AND RICH INTERNET APPLICATIONS
 

XML- XSL, XSLT, DOM ,RSS, Client Technologies- Adobe Flash, Flex, Microsoft Silverlight.

Unit-4
Teaching Hours:9
SERVER SIDE PROGRAMMING-I
 

Server side Programming – PHP- Passing variables between pages, Using tables, Form elements. Active server pages – Java server pages

Unit-5
Teaching Hours:9
SERVER SIDE PROGRAMMING-II & 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, 4th 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

 
  • To understand the concepts of machine learning
  • To appreciate supervised and unsupervised learning and their applications
  • To understand the theoretical and practical aspects of Probabilistic Graphical Models
  • To appreciate the concepts and algorithms of reinforcement learning
  • To learn aspects of computational learning theory

Learning Outcome

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

  • Implement a neural network for an application of your choice using an available tool
  • Implement probabilistic discriminative and generative algorithms for an application of your choice and analyze the results
  • Implement typical clustering algorithms for different types of applications
  • Design and implement an HMM for a sequence model type of application
  • Identify applications suitable for different types of machine learning with suitable justification

Unit-1
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 Bias-Variance Decomposition - Bayesian Linear Regression - Bayesian Model Comparison

Unit-2
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 -Feed-forward Network Functions - Error Backpropagation - Regularization - Mixture Density and Bayesian Neural Networks - Kernel Methods - Dual Representations - Radial Basis Function Networks. Ensemble methods- Bagging- Boosting.                            

Unit-3
Teaching Hours:9
UNSUPERVISED LEARNING
 

Clustering- K-means - EM - Mixtures of Gaussians - The EM Algorithm in General -Model selection for latent variable models - high-dimensional spaces -- The Curse of Dimensionality –Dimensionality Reduction - Factor analysis - Principal Component Analysis - Probabilistic PCA- Independent components analysis

Unit-4
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 classifiers-Markov 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

Unit-5
Teaching Hours:9
ADVANCED LEARNING
 

Sampling – Basic sampling methods – Monte Carlo. Reinforcement Learning - K-Armed Bandit - Elements - Model-Based Learning - Value Iteration - Policy Iteration. Temporal Difference Learning- Exploration Strategies- Deterministic and Non-deterministic 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:
  1. Christopher Bishop, “Pattern Recognition and Machine Learning” Springer, 2006
  2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
  3. Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
  4. Hastie, Tibshirani, Friedman, “The Elements of Statistical Learning” (2nd ed)., Springer, 2008
  5. Stephen Marsland, “Machine Learning –An Algorithmic Perspective”, CRC Press, 2009
Essential Reading / Recommended Reading

1.      Tom Mitchell, "Machine Learning", McGraw-Hill, 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

 
  • To provide students a systematic approach to initiate, plan, execute, control and close a software project.
  • To develop a good understanding of the nine project management areas, and  the role of a typical PM.
  • To equip students with understanding of the best practices, and techniques used in project management processes.
  • To enable students to gain a working knowledge of  ISO 9000 and CMMI, and process improvement techniques.

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.

Unit-1
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, Cost-benefit evaluation technology, Risk evaluation, Strategic program Management, Stepwise Project Planning.

 

Unit-2
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.

Unit-3
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.

 

Unit-4
Teaching Hours:9
Project Management and Control
 

Framework for Management and control, Collection of data Project termination, Visualizing progress, Cost monitoring, Earned Value Analysis-Project tracking, Change control-Software Configuration Management, Managing contracts, Contract Management.

Unit-5
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:
  1. Managing the Software Process by Watts S. Humphrey, published by Pearson Education 2012.
  2. Software Project Management, by Walker Royce, published by Pearson Education 2010.
Essential Reading / Recommended Reading
  1. An Introduction to the Team Software Process, by Watts S. Humphrey, Pearson Education 2012.
  2. A Discipline to Software Engineering by Watts S. Humphrey Pearson Education 2016.

      3.   Software Project Management in Practice by Pankaj Jalote, Pearson Education 2010.

      4.   Software Project Management Readings and Cases by Chris Kemerer 2010.

 

  • ISO standard. http://www.iso.ch
  • SEI.CMMI Tutorial, www.sei.cmu.edu/cmmi/publications/stc.presentations/tutorial.html
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 preparand present a topic on engineering/ technology, itis 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.

Learning Outcome

students towards acquiring competence in teaching, laboratoryskills, research methodologieand otherprofessional activities includingethics in the respective academicdisciplines.

The course will broadly cover the following aspects:

  • Teachingskills
  • Laboratoryskills andother professional activities
  • Researcmethodology

Unit-1
Teaching Hours:45
UNIT-1
 
COURSE NOTICES

 

 

 

 

 

Notices pertaining to this course will be displayed on the respective departmental notice boards by the panecoordinator/instructor.Students maalso check the exam notice board for notices issued by the exam division.

 

MAKEUPPOLICY All students are required to attend all the lectureand presentations in the panel.Participation and cooperation will also be taken intaccount in the finaevaluation. Requests for makeup should normallbavoided. However,in genuine cases,panel will decide action on a case bcase basis.

 

NOTE:Seminar shall be presented in the department in presencof a committee (Batch of Teachers)constituted by HOD.The seminar markare to be awardeby thcommittee. Students shall submit the seminareport in the prescribeStandard 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

§  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 short-term 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:

 

  • Get an inside view of an industry and organization/company
  • Gain valuable skills and knowledge
  • Make professional connections and enhance student's network
  • Get experience in a field to allow the student  to make a career transition

Learning Outcome

  • Get an inside view of an industry and organization/company
  • Gain valuable skills and knowledge
  • Make professional connections and enhance student's network
  • Get experience in a field to allow the student  to make a career transition

Unit-1
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 (PHASE-II) 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.

Unit-1
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