Department of COMPUTER SCIENCE AND ENGINEERING 

Syllabus for

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 

 
Unit1 
Teaching Hours:4 

Unit1


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

Unit2


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

Unit3


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

unit4


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

Unit5


skills are needed when writing the Methods, skills needed when writing the Results, skills are needed when writing the Discussion, skills are needed when writing the Conclusions, useful phrases, how to ensure paper is as good as it could possibly be the first time submission  
Text Books And Reference Books: Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)  
Essential Reading / Recommended Reading
 
Evaluation Pattern   
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. 
Unit1 
Teaching Hours:4 
INTRODUCTION


Disaster: Definition, Factors And Significance; Difference Between Hazard And Disaster; Natural And Manmade Disasters: Difference, Nature, Types And Magnitude.  
Unit2 
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, Manmade disaster: Nuclear Reactor Meltdown, Industrial Accidents, Oil Slicks And Spills, Outbreaks Of Disease And Epidemics, War And Conflicts.  
Unit3 
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; PostDisaster Diseases And Epidemics  
Unit4 
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.  
Unit5 
Teaching Hours:4 
RISK ASSESSMENT DISASTER RISK


Concept And Elements, Disaster Risk Reduction, Global And National Disaster Risk Situation. Techniques Of Risk Assessment, Global CoOperation 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 NonStructural 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 
Unit1 
Teaching Hours:8 
Alphabets in Sanskrit


Alphabets in Sanskrit, ∙ Past/Present/Future Tense, ∙ Simple Sentences  
Unit2 
Teaching Hours:8 
Order


Order ∙ Introduction of roots ∙ Technical information about Sanskrit Literature  
Unit3 
Teaching Hours:8 
Technical concepts of Engineering


Technical concepts of EngineeringElectrical, Mechanical, Architecture, Mathematics  
Text Books And Reference Books: "Abhyaspustakam” – Dr.Vishwas, SamskritaBharti Publication, New Delhi  
Essential Reading / Recommended Reading 1. “Teach Yourself Sanskrit” PrathamaDeekshaVempatiKutumbshastri, 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 
Unit1 
Teaching Hours:5 
Values and selfdevelopment


Values and selfdevelopment –Social values and individual attitudes. Work ethics, Indian vision of humanism. Moral and non moral valuation. Standards and principles. ∙ Value judgements  
Unit2 
Teaching Hours:5 
Importance of cultivation of values


Importance of cultivation of values. ∙ Sense of duty. Devotion, Selfreliance. Confidence, Concentration. Truthfulness, Cleanliness. ∙ Honesty, Humanity. Power of faith, National Unity. ∙Patriotism.Love for nature,Discipline  
Unit3 
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 selfdestructive habits. ∙ Association and Cooperation. ∙ Doing best for saving nature  
Unit4 
Teaching Hours:5 
Character and Competence


Character and Competence –Holy books vs Blind faith. ∙ Selfmanagement and Good health. ∙ Science of reincarnation. ∙ Equality, Nonviolence,Humility, Role of Women. ∙ All religions and same message. ∙ Mind your Mind, Selfcontrol. ∙ 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 

 
Unit1 
Teaching Hours:30 
na


na  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern Head of the Department will assign a suitable instructor/faculty member to each student. Students and faculty members covering a broad area will be grouped in a panel consisting of 45 students and 45 faculty members. Within one week after registration, the student should plan the details of the topics of lectures, laboratory experiments, developmental activities and broad topic of research etc in consultation with the assigned instructor/faculty. The student has to submit two copies of the written outline of the total work to the instructor within one week.
In a particular discipline, Instructors belonging to the broad areas will form the panel and will nominate one of them as the panel coordinator. The coordinator together with the instructors will draw a complete plan of lectures to be delivered by all students in a semester. Each student will present 3 4 lectures, which will be attended by all other students and Instructors. These lectures will be evenly distributed over the entire semester. The coordinator will announce the schedule for the entire semester and fix suitable meeting time in the week.
Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography.
A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student.
The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student.
A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated.
Wherever necessary and feasible, the panel coordinator in consultation with the concerned group may also seek participation of the faculty members from other groups in lectures and comprehensive viva.
Mid semester report and final evaluation report should be submitted in the 9^{th }week and 15^{th }week of the semester respectively. These should contain the following sections:
● Section (A): Lecture notes along with two question papers each of 180 min duration, one quiz paper (CIAI) of 120 min duration on the topics of lectures. The question paper should test concepts, analytical abilities and grasp of the subject. Solutions of questions also should be provided. All these will constitute lecture material. ● Section (B): Laboratory experiments reports and professional work report.
● Section (C): Research proposal with detailed references and bibliography in a standard format.
Wherever necessary, respective Head of the Departments could be approached by Instructors/panel coordinators for smooth operation of the course. Special lectures dealing with professional ethics in the discipline may also be arranged by the group from time to time.  
MTCS131  RESEARCH METHODOLOGY AND IPR (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. 
Unit1 
Teaching Hours:9 

Introduction to Research Methodology


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

Literature Review and Research Problem Identification


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

Data Collection & Analysis


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

Research Problem Solving


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

IPR and Research Writing


IPR: Invention and Creativity Intellectual PropertyImportance and Protection of Intellectual Property Rights (IPRs) A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation  Layout, structure and language of the report – Illustrations, figures, equations and tables.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading 1. Bjorn Gustavii, “How to Write and Illustrate Scientific Papers “ Cambridge University Press, 2/e. 2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell John wiley & sons, 1/e, 2013. 3. .James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.  
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks  
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 


Unit1 
Teaching Hours:9 

INTRODUCTION AND CONCEPTUAL MODELING


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

SQL Fundamentals


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

SQL Fundamental 2


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

DISTRIBUTED DATABASE


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

DATA WAREHOUSING


Introduction to Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Implementation, Data Warehousing to Data Mining, KDD process.  
Text Books And Reference Books:
 
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 : 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 


Unit1 
Teaching Hours:9 

INTRODUCTION


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

SEARCHING TECHNIQUES


ProblemSolving Agents, Welldefined problems and solutions, Formulating problems, Realworld problems. Uninformed Search Strategies, Breadthfirst search, Uniformcost search, Depthfirst search, Depthlimited search, Iterative deepening depthfirst search, Bidirectional search, Informed (Heuristic) Search Strategies, Greedy bestfirst 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.  
Unit3 
Teaching Hours:9 

Game Playing


Games, Optimal Decisions in Games,The minimax algorithm,Optimal decisions in multiplayer games, Alpha Beta Pruning, Move ordering, Imperfect RealTime Decisions,Cutting off search, Forward pruning.Stochastic Games,Evaluation functions for games of chance, Partially Observable Games, Krieg spiel: Partially observable chess,Card games, StateoftheArt Game Programs, Alternative Approaches  
Unit4 
Teaching Hours:9 

STATISTICAL AND REINFORCEMENT


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

DEEP LEARNING


Convolutional Neural Networks, Motivation, Convolution operations, Pooling, Image classification, Modern CNN architectures, Recurrent Neural Network, Motivation, Vanishing/Exploding gradient problem, Applications to sequences, Modern RNN architectures, Tuning/Debugging Neural Networks, Parameter search, Overfitting, Visualizations, Pretrained Models  
Text Books And Reference Books: Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd Edition, Pearson Education, 2014. Rich and Kevin Knight, “Artificial Intelligence”, 3^{rd} Edition, Tata McGrawHill, 2012. Francois Chollet “Deep Learning with Python”, 1^{st} Edition Manning Publication, 2018  
Essential Reading / Recommended Reading Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, 1^{st} Edition, Harcourt Asia Pvt. Ltd., 2012. George F. Luger, “Artificial IntelligenceStructures and Strategies for Complex Problem Solving”, 6^{th} Edition, Pearson Education / PHI, 2009.  
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks  
MTITDA133  ADVANCED DATA MINING (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 handson introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management. 

Learning Outcome 


Unit1 
Teaching Hours:9 

Introduction


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

Pattern Mining


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

Classification Methods


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

Cluster Analysis


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

Outlier Detection


ProximityBased Methods, and ClusteringBased Methods, Outlier Detection in HighDimensional Data. Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis.  
Text Books And Reference Books: Han J. &Kamber, M, “Data Mining: Concepts and Techniques”, Third Edition, Morgan Kaufmann, 2012.  
Essential Reading / Recommended Reading
 
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, ProximityBased Methods, and ClusteringBased Methods, Outlier Detection in HighDimensional Data. Case Study: Data Mining Applications: Recommender Systems, Intrusion Detection and Prevention and Financial Data Analysis. 

Learning Outcome 


Unit1 
Teaching Hours:9 

Discrete Random Variables and Probability


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

Continuous Random Variables and Probability Distributions


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

Joint Probability Distributions


Two Discrete Random Variables, Multiple Discrete Random Variables, Two Continuous Random Variables, Multiple Continuous Random Variables, Covariance and Correlation, Bivariate Normal Distribution, Linear Combinations of Random Variables,  
Unit4 
Teaching Hours:9 

Random Sampling and Data Description, Statistical Intervals for a Single Sample


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

Tests of Hypotheses for a Single Sample, Statistical Inference for Two Samples


Hypothesis Testing, Tests on the Mean of a Normal Distribution, Variance Known, Tests on the Mean of a Normal Distribution, Variance Unknown, Tests on the Variance and Standard Deviation of a Normal Distribution, Tests on a Population Proportion, Inference For a Difference in Means of Two Normal Distributions, Variances Known, Inference For a Difference in Means of Two Normal Distributions, Variances Unknown, Inference on the Variances of Two Normal Distributions  
Text Books And Reference Books: Douglas C. Montgomery, George C. Runger, “Applied Statistics and Probability for Engineers”, Third edition, John Wiley & Sons, Inc., 2014 NinaZumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014.  
Essential Reading / Recommended Reading Rao V Dukkipati, “Probability and Statistics for Scientists and Engineers”, New Age International Publishers, First edition, 2012, Ronald E Walpole, Raymond H Myers, Sharon L Myers, Keying E Ye, “Probability and Statistics for Engineers and Scientists”, Ninth Edition, Pearson Education, 2013  
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks  
MTITDA151  ADVANCES IN DATABASE MANAGEMENT SYSTEMS LAB (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 


Unit1 
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:
 
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:
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:
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:
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:
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 handson introduction to the Advanced Data Mining concepts with an emphasis on features useful to Engineering, Business and Management. 

Learning Outcome 


Unit1 
Teaching Hours:60 

List of Experiments


1. Introduction to data mining tools 2. Analysis of the various datasets by using frequent pattern mining algorithms 3. Analysis of the various datasets by using clustering algorithms 4. Analysis of the various datasets by using classifier algorithms 5. Analysis of the various datasets by using outlier detection algorithms  
Text Books And Reference Books:
1.Han J. & Kamber, M, “Data Mining: Concepts and Techniques”, Third Edition, Morgan Kaufmann, 2012
 
Essential Reading / Recommended Reading 1. PangNing Tan, Michael Steinbach, Vipin Kumar, “Introduction to Data Mining” Pearson, First Edition, 2014. 2. Mohammed J.Zaki, Wagneermeira, “Data Mining and Analysis: Fundamental concepts and algorithms”, First Edition, Cambridge University Press India, 2015. 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 

 
Unit1 
Teaching Hours:8 
Unit1


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


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


Asan and Pranayam i) Various yog poses and their benefits for mind & body ii)Regularization of breathing techniques and its effectsTypes of pranayam  
Text Books And Reference Books: Yogic Asanas for Group TariningPartI” :Janardan Swami YogabhyasiMandal, Nagpur  
Essential Reading / Recommended Reading “Rajayoga or conquering the Internal Nature” by Swami Vivekananda, AdvaitaAshrama (Publication Department), Kolkata  
Evaluation Pattern   
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:


Learning Outcome 

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

Unit1 
Teaching Hours:30 

COURSE NOTICES


Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.
MAKEUPPOLICY: All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.
NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.  
Text Books And Reference Books: Selected domain related text book will be sugessted.  
Essential Reading / Recommended Reading Research papers for the selected domain  
Evaluation Pattern   
MTITDA231  BIG DATA ANALYTICS (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 mapreduce analytics using Hadoop and related tools 

Learning Outcome 


Unit1 
Teaching Hours:9 

UNDERSTANDING BIG DATA


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

NOSQL DATA MANAGEMENT


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

BASICS OF HADOOP


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

MAPREDUCE APPLICATIONS


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

HADOOP RELATED TOOLS


Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –cassandra examples – cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queriescase study.  
Text Books And Reference Books:
 
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  
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 



Learning Outcome 


Unit1 
Teaching Hours:9 

INTRODUCTION


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

CLASSIFICATION METHODS


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

GRAPHICAL AND SEQUENTIAL MODELS


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

CLUSTERING METHODS


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

NEURAL NETWORKS


Neural networks the perceptron algorithm multilayer perceptron’s back propagationnonlinear regression multiclass discrimination training procedures localized network structure dimensionality reduction interpretation.  
Text Books And Reference Books:
 
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  
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 



Learning Outcome 

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

DIGITAL IMAGE FUNDAMENTALS


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

IMAGE ENHANCEMENT & RESTORATION


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

IMAGE COMPRESSION & SEGMENTATION


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

REPRESENTATION AND DESCRIPTION


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

OBJECT RECOGNITION AND INTERPRETATION


Patterns and pattern classes  DecisionTheoretic methods  Structural methodsCase studies  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading Author Name(s), “Book title”, Edition, Publisher Name, Year (if it is old edition, reprint details should be given)  
Evaluation Pattern Assessment of each paper · Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks) · End Semester Examination(ESE) : 50% (50 marks out of 100 marks) Components of the CIA CIA I : Mid Semester Examination (Theory) : 25 marks CIA II : Assignments : 10 marks CIA III : Quizzes/Seminar/Case Studies/Project Work : 10 marks Attendance : 05 marks Total : 50 marks  
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 nonlinear programming. The focus of the course is on convex optimization though some techniques will be covered for nonconvex function optimization too. After an adequate introduction to linear algebra and probability theory, students will learn to frame engineering minima maxima problems in the framework of optimization problems. 

Learning Outcome 

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

Unit1 
Teaching Hours:9 
Mathematical preliminaries


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


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


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


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


Projection methods, KKT conditions, Nonlinear constrained optimization models  
Text Books And Reference Books: Introduction To Optimization 4Th Edition by Edwin K. P. Chong & Stanislaw H. Zak, Wiley India, 2017.  
Essential Reading / Recommended Reading Nonlinear Programming, 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 mapreduce analytics using Hadoop CO 6: Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data Analytics 
Unit1 
Teaching Hours:60 

List of projects


 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern For subjects having practical as part of the subject End semester practical examination : 25 marks Records : 05 marks Mid semester examination : 10 marks Class work : 10 marks Total : 50 marks  
MTITDA252  MACHINE LEARNING LAB (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 highperformance computing environment.To develop the skills in applying appropriate supervised, semisupervised or unsupervised learning algorithms for solving practical problems.


Learning Outcome 

Select realworld applications that needs machine learning based solutions. Implement and apply machine learning algorithms. Select appropriate algorithms for solving a particular group of realworld problems. Recognize the characteristics of machine learning techniques that are useful to solve realworld problems 
Unit1 
Teaching Hours:60 
List of Experiments


1.Exercises to solve the realworld problems using the following machine learning methods: Linear Regression Logistic Regression MultiClass Classification Neural Networks Support Vector Machines KMeans 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. ShaiShalevShwartz, Shai BenDavid, “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 