Department of COMPUTER SCIENCE AND ENGINEERING

Syllabus for
Master of Technology in Information Technology Specialization in Data Analytics
Academic Year  (2020)

 
1 Semester - 2020 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MTCS111E01 ENGLISH FOR RESEARCH PAPER WRITING 2 0 0
MTCS111E02 DISASTER MANAGEMENT 2 0 0
MTCS111E03 SANSKRIT FOR TECHNICAL KNOWLEDGE 2 0 0
MTCS111E04 VALUE EDUCATION 2 0 0
MTCS112 PROFESSIONAL PRACTICE - I 2 1 50
MTCS131 RESEARCH METHODOLOGY AND IPR 3 3 100
MTITDA131 ADVANCES IN DATABASE MANAGEMENT SYSTEM 3 3 100
MTITDA132 ADVANCED ARTIFICIAL INTELLIGENCE 3 3 100
MTITDA133 ADVANCED DATA MINING 3 3 100
MTITDA134 STATISTICAL FOUNDATIONS FOR DATA SCIENCE 3 3 100
MTITDA151 ADVANCES IN DATABASE MANAGEMENT SYSTEMS LAB 4 2 50
MTITDA152 ADVANCED DATA MINING LAB 4 2 50
2 Semester - 2020 - Batch
Paper Code
Paper
Hours Per
Week
Credits
Marks
MTCS212E03 STRESS MANAGEMENT BY YOGA 2 0 0
MTCS213 PROFESSIONAL PRACTICE - II 2 1 50
MTITDA231 BIG DATA ANALYTICS 3 3 100
MTITDA232 MACHINE LEARNING 3 3 100
MTITDA241E01 ADVANCED DIGITAL IMAGE PROCESSING 3 3 100
MTITDA242E05 OPTIMIZATION TECHINIQUES 3 3 100
MTITDA251 BIG DATA ANALYTICS LAB 4 2 50
MTITDA252 MACHINE LEARNING LAB 4 2 50

MTCS111E01 - ENGLISH FOR RESEARCH PAPER WRITING (2020 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

-

MTCS111E02 - DISASTER MANAGEMENT (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

1. learn to demonstrate a critical understanding of key concepts in disaster risk reduction and humanitarian response.

 2. critically evaluate disaster risk reduction and humanitarian response policy and practice from multiple perspectives.

3. develop an understanding of standards of humanitarian response and practical relevance in specific types of disasters and conflict situations.

4. critically understand the strengths and weaknesses of disaster management approaches, planning and programming in different countries, particularly their home country or the countries they work in

Learning Outcome

Demonstrate a critical understanding of key concepts in disaster risk reduction and humanitarian response.

Unit-1
Teaching Hours:4
INTRODUCTION
 

Disaster: Definition, Factors And Significance; Difference Between Hazard And Disaster; Natural And Manmade Disasters: Difference, Nature, Types And Magnitude.

Unit-2
Teaching Hours:4
REPERCUSSIONS OF DISASTERS AND HAZARDS
 

Economic Damage, Loss Of Human And Animal Life, Destruction Of Ecosystem. Natural Disasters: Earthquakes, Volcanisms, Cyclones, Tsunamis, Floods, Droughts And Famines, Landslides And Avalanches, Man-made disaster: Nuclear Reactor Meltdown, Industrial Accidents, Oil Slicks And Spills, Outbreaks Of Disease And Epidemics, War And Conflicts.

Unit-3
Teaching Hours:4
DISASTER PRONE AREAS IN INDIA STUDY OF SEISMIC ZONES
 

Areas Prone To Floods And Droughts, Landslides And Avalanches; Areas Prone To Cyclonic And Coastal Hazards With Special Reference To Tsunami; Post-Disaster Diseases And Epidemics

Unit-4
Teaching Hours:4
DISASTER PREPAREDNESS AND MANAGEMENT PREPAREDNESS
 

Monitoring Of Phenomena Triggering A Disaster Or Hazard; Evaluation Of Risk: Application Of Remote Sensing, Data From Meteorological And Other Agencies, Media Reports: Governmental And Community Preparedness.

Unit-5
Teaching Hours:4
RISK ASSESSMENT DISASTER RISK
 

Concept And Elements, Disaster Risk Reduction, Global And National Disaster Risk Situation. Techniques Of Risk Assessment, Global Co-Operation In Risk Assessment And Warning, People’s Participation In Risk Assessment. Strategies for Survival. Disaster Mitigation Meaning, Concept And Strategies Of Disaster Mitigation, Emerging Trends In Mitigation. Structural Mitigation And Non-Structural Mitigation, Programs Of Disaster Mitigation In India.

Text Books And Reference Books:

R. Nishith, Singh AK, “Disaster Management in India: Perspectives, issues and strategies “’New Royal book Company.

Essential Reading / Recommended Reading

1.      Sahni, PardeepEt.Al. (Eds.),” Disaster Mitigation Experiences And Reflections”, Prentice Hall Of India, New Delhi.

       2.   Goel S. L., Disaster Administration And Management Text And Case Studies”,Deep&Deep Publication Pvt. Ltd., New Delhi.

Evaluation Pattern

NA

MTCS111E03 - SANSKRIT FOR TECHNICAL KNOWLEDGE (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

1. To get a working knowledge in illustrious Sanskrit, the scientific language in the world

2. Learning of Sanskrit to improve brain functioning

3. Learning of Sanskrit to develop the logic in mathematics, science & other subjects

4. enhancing the memory power

5. The engineering scholars equipped with Sanskrit will be able to explore the 6. huge knowledge from ancient literature

Learning Outcome

Demonstrate Sanskrit to develop the logic in mathematics, science & other subjects

Unit-1
Teaching Hours:8
Alphabets in Sanskrit
 

Alphabets in Sanskrit, Past/Present/Future Tense, Simple Sentences

Unit-2
Teaching Hours:8
Order
 

Order Introduction of roots Technical information about Sanskrit Literature

Unit-3
Teaching Hours:8
Technical concepts of Engineering
 

Technical concepts of Engineering-Electrical, Mechanical, Architecture, Mathematics

Text Books And Reference Books:

"Abhyaspustakam” – Dr.Vishwas, Samskrita-Bharti Publication, New Delhi 

Essential Reading / Recommended Reading

1. “Teach Yourself Sanskrit” PrathamaDeeksha-VempatiKutumbshastri, Rashtriya Sanskrit Sansthanam, New Delhi Publication

2. “India’s Glorious Scientific Tradition” Suresh Soni, Ocean books (P) Ltd., New Delhi.

Evaluation Pattern

NA

MTCS111E04 - VALUE EDUCATION (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:0
Credits:0

Course Objectives/Course Description

 

Understand value of education and self- development

Imbibe good values in students

Let the should know about the importance of character

Learning Outcome

Explain the value of education and self- development

Unit-1
Teaching Hours:5
Values and self-development
 

Values and self-development –Social values and individual attitudes. Work ethics, Indian vision of humanism. Moral and non- moral valuation. Standards and principles. Value judgements

Unit-2
Teaching Hours:5
Importance of cultivation of values
 

Importance of cultivation of values. Sense of duty. Devotion, Self-reliance. Confidence, Concentration. Truthfulness, Cleanliness. Honesty, Humanity. Power of faith, National Unity. Patriotism.Love for nature,Discipline

Unit-3
Teaching Hours:5
Personality and Behavior Development
 

Personality and Behavior Development - Soul and Scientific attitude. Positive Thinking. Integrity and discipline. Punctuality, Love and Kindness. Avoid fault Thinking. Free from anger, Dignity of labour. Universal brotherhood and religious tolerance. True friendship. Happiness Vs suffering, love for truth. Aware of self-destructive habits. Association and Cooperation. Doing best for saving nature

Unit-4
Teaching Hours:5
Character and Competence
 

Character and Competence –Holy books vs Blind faith. Self-management and Good health. Science of reincarnation. Equality, Nonviolence,Humility, Role of Women. All religions and same message. Mind your Mind, Self-control. Honesty, Studying effectively

Text Books And Reference Books:

Chakroborty, S.K. “Values and Ethics for organizations Theory and practice”, Oxford University Press, New Delhi

Essential Reading / Recommended Reading

NA

Evaluation Pattern

NA

MTCS112 - PROFESSIONAL PRACTICE - I (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
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 (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The aim of 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

MTITDA131 - ADVANCES IN DATABASE MANAGEMENT SYSTEM (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

·         To understand the fundamentals of DBMS along with design concept.

·         To learn the fundamental SQL commands and its applications in Databases.

·         To study the distributed databases and its application.

·         To understand the data warehousing concepts

Learning Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Explain the fundamentals of Database systems

L2

2.

Illustrate the basic concepts of SQL

L3

3.

Examine the advanced concepts of SQL

L4

4.

Examine the concepts of distributed database

L4

5.

Demonstrate the fundamentals of data warehousing

L2

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 .
  2. Carlos Coronel & Steven Morris, “Database Systems: Design, Implementation, & Management”, February 4, 2014.
Essential Reading / Recommended Reading
  1. 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   :   Closed Book Test and Quiz                                                 : 10 marks                  

CIA II  :  Mid Semester Examination (Theory)                                   : 25 marks

CIA III  : Closed Book Test and Quiz                                                  : 10 marks

                        Attendance                                                                             : 05 marks

                                   Total                                                                                       : 50 marks

 

 

MTITDA132 - ADVANCED ARTIFICIAL INTELLIGENCE (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

This course 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.

L3

Explain various searching techniques.

L2

Apply Game playing techniques for the real time problems.

L3

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

L2

Explore Deep Learning models to image and text processing application

L3

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
SEARCHING TECHNIQUES
 

Problem-Solving Agents, Well-defined problems and solutions, Formulating problems, Real-world problems. Uninformed Search Strategies, Breadth-first search, Uniform-cost search, Depth-first search, Depth-limited search, Iterative deepening depth-first search, Bidirectional search, Informed (Heuristic) Search Strategies, Greedy best-first search, A* search: Minimizing the total estimated solution cost, Heuristic Functions. The effect of heuristic accuracy on performance. Beyond Classical Search, Local Search Algorithms and Optimization Problems, Local Search in Continuous Spaces.

Unit-3
Teaching Hours:9
Game Playing
 

Games, Optimal Decisions in Games,The minimax algorithm,Optimal decisions in multiplayer games, Alpha Beta Pruning, Move ordering, Imperfect Real-Time Decisions,Cutting off search, Forward pruning.Stochastic Games,Evaluation functions for games of chance, Partially Observable Games, Krieg spiel: Partially observable chess,Card games, State-of-the-Art Game Programs, Alternative Approaches

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 (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
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.
  3. 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 (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
Max Marks:100
Credits:3

Course Objectives/Course Description

 

The students will learn basics of statistics applied in Data Science, 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 (2020 Batch)

Total Teaching Hours for Semester:60
No of Lecture Hours/Week:4
Max Marks:50
Credits:2

Course Objectives/Course Description

 

·         To understand the fundamentals of DBMS along with design concept.

·         To learn the fundamental SQL commands and its applications in Databases.

·         To study the distributed databases and its application.

·         To understand the data warehousing concepts

Learning Outcome

Sl NO

DESCRIPTION

REVISED BLOOM’S TAXONOMY (RBT)LEVEL

1.

Explain the fundamentals of Database systems

L2

2.

Illustrate the basic concepts of SQL

L3

3.

Examine the advanced concepts of SQL

L4

4.

Examine the concepts of distributed database

L4

5.

Demonstrate the fundamentals of data warehousing

L2

Unit-1
Teaching Hours:60
List of Experiments
 

1.     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:
  1.      Abraham Silberschatz, Henry. F. Korth, S.Sudharsan, “Database System Concepts”, 6th Edition. Tata      McGraw Hill, 2010 .
  2.    Carlos Coronel & Steven Morris, “Database Systems: Design, Implementation, & Management”, February 4, 2014.
Essential Reading / Recommended Reading

-

Evaluation Pattern

            End semester practical examination                                     : 25 marks

            Records                                                                                      : 05 marks

            Observation                                                                              : 10 marks 

            Mid semester examination                                                     : 10 marks

                                                                                Total                       : 50 marks 

 

LAB COMPONENT EVALUATION RUBRICS

 

OBSERVATION EVALUATION

DETAILS       :  Observation

DESCRIPTION         :  Student has to write the program in the observation as well as in record

ASSIGNMENT TYPE :  Individual

ASSIGNMENT DETAILS: 

1.      A Common Questions will be given to each student.

2.      After implementation and execution of the proposed solution, the result has to be recorded in the observation.

3.      Students have to complete their observations and get it evaluated by their respective faculty members during the same lab. In case a student is absent due to a genuine reason, he/she will complete the experiment, and show it to the respective teachers before the next lab.

SUBMISSION FORMAT: Observation Note book

MARKS                     : 20

TENTATIVE DATE : All Lab Hrs.

Venue                         : Computer Lab

EVALUATION RUBRICS:

Skills

Excellent  

(90-100%)

Good

(70-89%)

Average

(40-69%)

 

Needs Improvement (<40%)

Algorithm

Efficient design and  implemented based on scenario

Efficient design but lacking in   implementation

Lacking in both Design and Implementation

No efficient design and poor implementation

Output

Appropriate Results with formatting.

Appropriate Results without formatting

Mistakes in the output

No logical results

 

 

RECORD EVALUATION

DETAILS                   :  Record

DESCRIPTION         :  Student has to record everything in the record note book

ASSIGNMENT TYPE:  Individual

ASSIGNMENT DETAILS: 

1.       Each student has to record the aim, algorithm, program what they executed in the lab with output and result.

2.      Record will be corrected in the Lab itself.

3.      Viva questions will be asked and marks will be awarded for that.

4.      Lab record needs to be completed when the student comes for the next lab. In case it is not completed, it will be evaluated for 80% of the marks.

SUBMISSION FORMAT: Record

MARKS                     : 10

TENTATIVE DATE : All Lab Hours

Venue : Computer Lab.       

EVALUATION RUBRICS:

 

Skills

Excellent  

(90-100%)

Good 

(70-89%)

Average

(40-69%)

 

Needs Improvement (<40%)

Correctness of algorithm, code and output

Appropriate format

10 -20% mistake in the format

21-40% mistake in the format

Above 40% mistake in the format.

 

MSE EVALUATION

DETAILS       :  MSE

DESCRIPTION         :  Mid Term Lab Examination

ASSIGNMENT TYPE :  Individual

ASSIGNMENT DETAILS: 

1.      Questions will be given to students. Students have to design the solution and implement it in C.

2.      According to the implementation, Marks will be awarded.

SUBMISSION FORMAT    : MSE Answer Script and Lab execution

MARKS                                 : 50

TENTATIVE DATE             : MSE Examination Time Table

Venue                                     :  Lab

EVALUATION RUBRICS:

CRITERIA

Excellent  

(90-100%)

Good

 (70-89%)

Average

(40-69%)

 

Needs Improvement (<40%)

Write up(20)

100% Understanding the problem, Efficient design, Appropriate Logic

Understanding the problem, good design, Appropriate Logic

Understanding the problem, Average Design, 30 % of Logic Error.

incomplete

Execution(20)

Full Execution with formatted results

Full execution

Partial execution

No execution

Viva(10)

All questions answered

Atleast 70% questions answered

Atleast 50% questions answered

Less than 50% questions answered

 

 

ESE EVALUATION

DETAILS       :  ESE

DESCRIPTION         :  End sem Lab Examination

ASSIGNMENT TYPE :  Individual

ASSIGNMENT DETAILS: 

1.      Questions will be given to students. Students have to design the solution and implement it in C.

2.      According to the implementation, Marks will be awarded.

SUBMISSION FORMAT    : ESE Answer Script and Lab execution

MARKS                                 : 50

TENTATIVE DATE             : ESE Examination Time Table

Venue                                     :  Lab

EVALUATION RUBRICS:

 

CRITERIA

Excellent  

(90-100%)

Good

(70-89%)

Average

(40-69%)

 

Needs Improvement (<40%)

Write up(20)

100% Understanding the problem, Efficient design, Appropriate Logic

Understanding the problem, good design, Appropriate Logic

Understanding the problem, Average Design ,30 % of Logic Error.

incomplete

Execution(20)

Full Execution with formatted results

Full execution

Partial execution

No execution

Viva(10)

All questions answered

Atleast 70% questions answered

Atleast 50% questions answered

Less than 50% questions answered

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

MTITDA152 - ADVANCED DATA MINING LAB (2020 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

  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: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:

 

1.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.

3. Ian H. Witten, &Eibe Frank, “Data Mining –Practical Machine Learning Tools and Techniques”, 3rd Edition, Elesvier, 2011.

Evaluation Pattern

End semester practical examination                                     : 25 marks

            Records                                                                                   : 05 marks

            Mid semester examination                                                    : 10 marks

            Class work                                                                              : 10 marks

            Total                                                                                       : 50 marks

MTCS212E03 - STRESS MANAGEMENT BY YOGA (2020 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

-

MTCS213 - PROFESSIONAL PRACTICE - II (2020 Batch)

Total Teaching Hours for Semester:30
No of Lecture Hours/Week:2
Max Marks:50
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 (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
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.
  2. 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.
  4. 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 (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
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.
  2. John Mueller and Luca Massaron, “Machine Learning For Dummies“, John Wiley & Sons, 2016.
Essential Reading / Recommended Reading
  1. 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 (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
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

MTITDA242E05 - OPTIMIZATION TECHINIQUES (2020 Batch)

Total Teaching Hours for Semester:45
No of Lecture Hours/Week:3
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 (2020 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

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: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:
  1. Tom White, "Hadoop: The Definitive Guide", 4th  Edition, O'Reilley, 2012.
  2. 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.
  4. Alan Gates, "Programming Pig", O'Reilley, 2011
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 (2020 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

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:60
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

2.Develop programs to implement Anomaly Detection & Recommendation Systems.

3.Implement GPU computing models to solving some of the problems mentioned in Problem 1.

Text Books And Reference Books:

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.

E. Alpaydin, “Machine Learning”, MIT Press, 2010.

C. Bishop, “Pattern Recognition and Machine Learning, Springer”, 2006.

ShaiShalev-Shwartz, Shai Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge University Press, 2014. 

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