M.Tech. (Full Time) - Knowledge Engineering …. (Full Time) - Knowledge Engineering . Curriculum & Syllabus . 2015 – 2016 . DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING . FACULTY

M.Tech. (Full Time) - Knowledge Engineering Curriculum & Syllabus

2015 – 2016

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING FACULTY OF ENGINEERING AND TECHNOLOGY

SRM UNIVERSITY SRM NAGAR, KATTANKULATHUR – 603 203

1 SRM-M.Tech-Knowledge Engg.-2015-16

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING M.Tech - KNOWLEDGE ENGINEERING

CURRICULUM 2015 -2016

Course Code Course Name L T P C

SEMESTER I CS2021 Artificial Intelligence & Intelligent systems 3 0 2 4 CS2022 Knowledge Based System Design 4 0 0 4 CS2023 Data & Knowledge Mining 4 0 0 4

CAC2001 Career Advancement Course For Engineers - I 1 0 1 1 Elective- I 3 0 0 3 Elective- II 3 0 0 3

TOTAL 18 0 3 19 Total Contact Hours: 21

SEMESTER II CS2024 Semantic Web 4 0 0 4 CS2025 Knowledge Based Neural Computing 3 0 2 4 CS2026 Agent Based Learning 4 0 0 4

CAC2002 Career Advancement Course For Engineers - II 1 0 1 1 Elective- III 3 0 0 3 Elective- IV 3 0 0 3


SEMESTER III Elective- V 3 0 0 3 Elective- VI 3 0 0 3 CAC2003 Career Advancement Course For Engineers - III 1 0 1 1 CS2047 Seminar 0 0 1 1 CS2049 Project Work Phase I 0 0 12 6


SEMESTER IV CS2050 Project Work Phase II 0 0 32 16

SEMESTER I-III Supportive course( 1 course of 3 credits in I or II or III sem.) 3 0 0 3


Interdisciplinary Elective (1 course of 3 credits in I or II or III sem.) 3 0 0 3


Total credits to be earned for the award of M.Tech degree – 74 CONTACT HOUR/CREDIT: L: Lecture Hours per week T: Tutorial Hours per week P: Practical Hours per week C: Credit

PROGRAM ELECTIVES

Course Code Name Of The Course L T P C

CS2104 Digital Image Processing 3 0 0 3 CS2108 Pattern Recognition Techniques 3 0 0 3 CS2109 Data Warehousing and its Applications 3 0 0 3 CS2121 Machine Vision 3 0 0 3 CS2122 Decision Support Systems 3 0 0 3 CS2123 Human Interface System Design 3 0 0 3 CS2124 Reasoning Under Uncertainty 3 0 0 3 CS2125 Speech and Language Processing 3 0 0 3 CS2126 Deductive and Inductive Reasoning 3 0 0 3 CS2127 Bio-Informatics 3 0 0 3 CS2128 Spatio-Temporal Reasoning 3 0 0 3

SUPPORTIVE COURSES

Course Code Name of The Course L T P C

MA2013 Mathematical Foundations of Computer Science 3 0 0 3 MA2010 Graph Theory and Optimization Techniques 3 0 0 3 MA2011 Stochastic Processes and Queueing Theory 3 0 0 3


NOTE: Students have to register for the courses as per the following guidelines:

Sl. No. Category

Credits

I Semester II Semester III Semester IV

Semester Category

Total 1. Core courses 12 ( 3

courses) 12 ( 3

courses) --- --- 24

2. Program Elective courses

18 (in I to III semesters) --- 18

3. Interdisciplinary elective courses (any one program elective from other programs)

3 (One course to be taken in Semester I or II or III)

3

4. Supportive courses - mandatory

3 (One course to be taken in Semester I or II or III)

--- 3

5. Seminar --- --- 1 --- 1 6. Career

Advancement Courses

1 1 1 -- 3

7. Project work --- --- 06 16 22 Total 74


SEMESTER I

CS2021

ARTIFICIAL INTELLIGENCE AND INTELLIGENT SYSTEMS L T P C

Total Contact Hours - 75 3 0 2 4 Prerequisite Nil

PURPOSE The purpose of this course is to give students a comprehensive understanding of Artificial Intelligence and Intelligent Systems in the context of Knowledge Engineering. INSTRUCTIONAL OBJECTIVES 1. To provide a strong foundations of fundamental concepts in Artificial Intelligence 2. To get familiar with the various applications of these techniques in Intelligent

Systems UNIT I - AI INTRODUCTION (10 hours) Introduction -Intelligent Agents -Problem Solving -Solving Problems by Searching - Beyond Classical Search - Adversarial Search - Constraint Satisfaction Problems. UNIT II - KNOWLEDGE AND REASONING (17 hours) Logical Agents -First-Order Logic - Inference in First-Order Logic -Classical Planning - Planning and Acting in the Real World -Knowledge Representation. UNIT III - UNCERTAIN KNOWLEDGE AND REASONING (20 hours) Quantifying Uncertainty -Probabilistic Reasoning - Probabilistic Reasoning over Time -Making Simple Decisions -Making Complex Decisions. UNIT IV - LEARNING (17 hours) Learning from Examples - Knowledge in Learning - Learning Probabilistic Models -Reinforcement Learning -Communicating, Perceiving, and Acting-Natural Language Processing - Natural Language for Communication- Perception UNIT V - EXPERT SYSTEM (11 hours) Defining Expert Systems – Expert system architecture-Robot Architectures REFERENCES 1. Stuart Russel and Peter Norwig, “Artificial Intelligence: A Modern Approach”,

Prentice Hall third edition, 2012. 2. Kevin Knight, Eline Rich B.Nair,“Artificial Intelligence”, McGraw Hill Education 3rd

edition 2012.


CS2022

KNOWLEDGE BASED SYSTEM DESIGN L T P C Total Contact Hours – 60 4 0 0 4 Prerequisite Nill

PURPOSE To learn the Knowledge based system design techniques. INSTRUCTIONAL OBJECTIVES 1. To learn the concepts of knowledge base and inference engine. 2. Expert Systems, Architecture and Programming. 3. Machine Learning.

UNIT I – INTRODUCTION (12 hours) Introduction To Knowledge Engineering : The Human Expert And An Artificial Expert – Knowledge Base And Inference Engine – Knowledge Acquisition And Knowledge Representation. UNIT II – PROBLEM SOLVING (12 hours) Problem Solving Process: Rule Based Systems – Heuristic Classifications – Constructive Problem Solving. UNIT III- EXPERT SYSTEMS (12 hours) Tools For Building Expert Systems - Case Based Reasoning – Semantic Of Expert Systems – Modeling Of Uncertain Reasoning – Applications Of Semiotic Theory; Designing For Explanation. UNIT IV- EXPERT SYSTEM ARCHITECTURE AND PROGRAMMING (12 hours) Expert System Architectures - High Level Programming Languages – Logic Programming For Expert Systems. UNIT V - MACHINE LEARNING (12 hours) Machine Learning – Rule Generation And Refinement –Learning Evaluation – Testing And Tuning. REFERENCES 1. Peter Jackson, ”Introduction to Expert Systems”, 3rd Edition, Pearson Education

2007. 2. Robert I. Levine, Diane E. Drang, Barry Edelson: “ AI and Expert Systems: a

comprehensive guide, C language”, 2nd edition, McGraw-Hill 1990. 3. Jean-Louis Ermine: “Expert Systems: Theory and Practice”, 4th printing,

Prentice-Hall of India , 2001.


4. Stuart Russell, Peter Norvig: “Artificial Intelligence: A Modern Approach”,2nd Edition,Pearson Education, 2007.

5. Padhy .N.P, “Artificial Intelligence and Intelligent Systems”,4th impression, Oxford University Press, 2007.

CS2023

DATA & KNOWLEDGE MINING L T P C Total Contact Hours - 60 4 0 0 4 Prerequisite Nill

PURPOSE This course provides a complete overview of Data mining and knowledge mining techniques. INSTRUCTIONAL OBJECTIVES 1. To understand the concepts of Data Mining. 2. To understand Classification and prediction and cluster analysis techniques. 3. To understand Applications of Data and knowledge mining.

UNIT I - INTRODUCTION (12 hours) Introduction to Data Mining – Kind of Data – Functionalities – Interesting Patterns – Task Primitives – Issues-In Data Mining - Data Preprocessing: Why Preprocessing? – Data Summarization – Data Cleaning, Integration, Transformation, Reduction.

UNIT II - MINING FREQUENT PATTERNS (12 hours) Mining Frequent Patterns: Associations And Correlations - Basic Concepts – Frequent Item Set Mining Methods – Mining Various Kinds Of Association Rules – Correlation Analysis – Constraint Based Association Mining.. UNIT III - CLASSIFICATION AND PREDICTION (12 hours) Classification and Prediction: Issues Regarding Classification And Prediction – Decision Tree Induction Classification – Bayesian, Rule Based Classification – Support Vector Machine -Prediction: Linear, Non-Linear Regression – Accuracy and Error Measures. UNIT IV - CLUSTER ANALYSIS (12 hours) Cluster Analysis: What Is Cluster Analysis? Types Of Data In Cluster Analysis – A Categorization Of Major Clustering Methods – Hierarchical Methods – Model Based Methods – Constraint Based Cluster Analysis. UNIT V - APPLICATIONS AND TRENDS IN DATA MINING (12 hours) Applications and Trends in Data Mining: Data Mining Applications – Products And Research Prototypes –Additional Themes on Data Mining – Social Impacts of Data Mining – Trends in Data Mining..


REFERENCES 1. Jiawei Han and Micheline Kamber, “Data Mining – Concepts and Techniques”,

Second Edition, Morgan Kaufmann Publishers, 2006. 2. Dunham .M.H, “Data Mining: Introductory and Advanced Topics”, Pearson

Education, 2001. 3. Hand .D, Mannila H. and Smyth P., “Principles of Data Mining”, Prentice-Hall.

2001. 4. Witten .I.H, and Frank E., “Data Mining: Practical Machine Learning Tools and

Techniques”, Morgan Kaufmann. 2000.

ELECTIVE – I L T P C Total Contact Hours - 45 3 0 0 3

Students to choose one Elective course from the list of courses mentioned in the curriculum

ELECTIVE – II L T P C Total Contact Hours - 45 3 0 0 3



SEMESTER II

CS2024

SEMANTIC WEB L T P C Total Contact Hours – 60 4 0 0 4 Prerequisite Nill

PURPOSE This course provides a complete overview of semantic Web and its Applications INSTRUCTIONAL OBJECTIVES 1. To understand the concepts of Semantic Web. 2. To understand the characteristics of the agents. 3. To understand design and implementation of Agents. 4. To understand the implementation described in the architecture level.

UNIT I - INTRODUCTION (12 hours) The world of the semantic web-WWW-meta data-Search engine-Search engine for traditional web-Semantic web-Search engine for semantic web-Traditional web to semantic web.

UNIT II - SEMANTIC WEB TECHNOLOGY (12 hours) RDF-Rules of RDF-Aggregation-Distributed information-RDFS-core elements of RDFS-Ontology-Taxonomy-Inferencing based on RDF schema

UNIT III - OWL (12 hours) OWL-Using OWL to define classes-Set operators-Enumerations-Define properties-ontology matching-Three faces of OWL-Validate OWL.

UNIT IV- SWOOGLE (12 hours) Swoogle-FOAF-Semantic markup-Issues-prototype system-Design of Semantic web search engine-Discovery and indexation-prototype system-case study.

UNIT V - SEMANTIC WEB SERVICES (12 hours) Semantic web services-OWL-S-Upper ontology-WSDL-S,OWL-S to UDDI mapping ,Design of the search engine,implementations. REFERENCES 1. Liyang Yu , “Introduction to the Semantic Web and Semantic web services”

Chapman & Hall/CRC, Taylor & Francis group, 2007. 2. Johan Hjelm, “Creating the Semantic Web with RDF“, Wiley,2001 3. Grigoris Antoniou and Frank van Harmelen, “A Semantic Web Primer”, MIT

Press, 2012. CS2025 KNOWLEDGE BASED NEURAL COMPUTING L T P C


Total Contact Hours - 75 3 0 2 4 Prerequisite Nill

PURPOSE The purpose of this course is to impart the various techniques for integrating knowledge into Neural Networks. INSTRUCTIONAL OBJECTIVES 1. To be familiar with the various architectures and Techniques of Knowledge-

Based Neural Computing. 2. To learn the methods for extracting rules from recurrent neural networks. 3. To apply Data mining Techniques for Information Extraction from Neural

Networks. 4. To develop Hybrid Intelligent Systems.

UNIT I – ARCHITECTURES AND TECHNIQUES FOR KNOWLEDGE-BASED NEURO COMPUTING (15 hours) Overview of Neural Computing – The Knowledge–Data Trade-Off–Foundations for Knowledge-Based Neurocomputing – Techniques for building Prior Knowledge into Neural Networks – A Metalevel architecture for Knowledge-Based Neurocomputing – Implementation of Logic functions by Fixed weight Learning – Design and train a Feedforward Neural Network for Parity, Encoding and Symmetry Problems – Implementation of Delta learning Rule for a classification problem. UNIT II – SYMBOLIC KNOWLEDGE REPRESENTATION IN RECURRENT NEURAL NETWORKS (15 hours) Introduction and Theoretical Aspects of Neural Networks – Recurrent Architecture and models of Computation – Representation of Symbolic Knowledge in Neural Networks – Computational Models as Symbolic Knowledge – Mapping Automata into Recurrent Neural Networks – Extraction of rules from Recurrent Neural Networks – Implementation of Recurrent Neural Network Architecture. UNIT III – STRUCTURAL LEARNING AND RULE DISCOVERY (18 hours) Structural Learning Methods – Learning with Forgetting – Prediction of Time Series – Adaptive Learning – Rule Extraction and Discovery – Transformation of rules to ANN- Implementation of Adaptive learning-ART1 Architecture. UNIT IV – INTEGRATION OF HETEROGENEOUS SOURCES OF PARTIAL DOMAIN KNOWLEDGE AND DATAMINING TECHNIQUES (12 hours) Experts Integration –Domain and Range Transformations – Incremental Single Expert Expansion –Data mining Techniques – Introduction –Direct Information Extraction procedures –Indirect Information Extraction procedures - Examples.


UNIT V – EXTRACTION OF DECISION TREES AND LINGUISTIC RULES FROM ANN (15 hours) Extraction of Rules from Artificial Neural Networks – ANN-DT Algorithm –Fuzzy Rule Extraction Algorithm –Iris flower classification problem –Knowledge Representation in Expert Systems –Neural Networks in Expert Systems –Hybrid Systems-Neural Expert Systems – Implementation of Extraction of rules from ANN. REFERENCES 1. Ian Cloete, Jacek .M Zurada, “Knowledge-Based Neurocomputing”, Universities

Press (India) Ltd., First Edition, 2002. 2. Eyal Kolman, Michael Margaliot, “Knowledge-Based Neurocomputing: A Fuzzy

Logic Approach”, Springer Verlog, First Edition, 2009. 3. Martin T. Hagan, Howard B. Demuth, Mark Beale, “Neural Network Design”,

Thomson and Learning, Third Reprint 2003.

CS2026

AGENT BASED LEARNING L T P C Total Contact Hours - 60 4 0 0 4 Prerequisite Nill

PURPOSE The course gives a comprehensive understanding on software agents. INSTRUCTIONAL OBJECTIVES

1. The characteristics of the agents. 2. The design and implementation of Agents 3. The implementation described in the architecture level.

UNIT I – INTRODUCTION (12 hours) Introduction about Agents - Interacting with Agents – How Might people interact with Agents - Agent from Direct Manipulation to Delegation - Interface Agent Metaphor with Character. UNIT II – AGENT DESIGN AND COORDINATION (12 hours) Designing Agents - Direct Manipulation versus Agent Path to Predictable- Agents for Information Sharing and Coordination. UNIT III – AGENT IMPLEMENTATION Agents that Reduce Work Information Overhead - Agents without Programming Language - Life like Computer character - S/W Agents for cooperative Learning - Architecture of Intelligent Agents


UNIT IV - INFORMATION INTEGRATION (12 hours) Overview of Agent Oriented Programming - Agent Communication Language - Agent Based Framework of Interoperability - Agents for Information Gathering - Open Agent Architecture – Communicative Action for Artificial Agent.

UNIT V – MOBILE AGENT (12 hours) Mobile Agent Paradigm - Mobile Agent Concepts -Mobile Agent Technology - Case Study: Tele Script, Agent Tel. REFERENCES 1. Jeffrey M.Bradshaw, “Software Agents”, MIT Press, 2000. 2. William R. Cockayne, Michael Zyda, “Mobile Agents”, Prentice Hall, 1998. 3. Russel & Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, 2nd

Edition, 2002. 4. Joseph P.Bigus & Jennifer Bigus, “Constructing Intelligent agents with Java: A

Programmer's Wiley; 1 edition, 1998. 5. Guide to Smarter Applications ”, Wiley, 1997.

ELECTIVE - III L T P C Total Contact Hours - 45 3 0 0 3


ELECTIVE - IV L T P C Total Contact Hours - 45 3 0 0 3


SEMESTER III

ELECTIVE - V L T P C Total Contact Hours - 45 3 0 0 3

Students to choose one Elective course from the list of courses mentioned in the curriculum.

ELECTIVE - VI L T P C Total Contact Hours - 45 3 0 0 3


Students to choose one Elective course from the list of courses mentioned in the curriculum.

INTERDISCIPLINARY ELECTIVE L T P C Total Contact Hours - 45 3 0 0 3

Students to choose one Elective course from the list of Post Graduate courses specified under the Faculty of Engineering and Technology other than courses under M.Tech (CSE) curriculum either in I, II or III semester

SUPPORTIVE COURSE L T P C Total Contact Hours - 45 3 0 0 3

Students to choose one course from the list of supportive courses mentioned in the curriculum either in I, II or III semester

CS2047 L T P C SEMINAR 0 0 1 1

PURPOSE To train the students in preparing and presenting technical topics. INSTRUCTIONAL OBJECTIVE The student shall be capable of identifying topics of interest related to the program of study and prepare and make presentation before an enlightened audience. The students are expected to give at least two presentations on their topics of interest which will be assessed by a committee constituted for this purpose. This course is mandatory and a student has to pass the course to become eligible for the award of degree. Marks will be awarded out of 100 and appropriate grades assigned as per the regulations L T P C CS2049 PROJECT WORK PHASE I

(III SEMESTER) 0 0 12 6

CS2050 PROJECT WORK PHASE II (IV SEMESTER) 0 0 32 16

PURPOSE To undertake research in an area related to the program of study INSTRUCTIONAL OBJECTIVE The student shall be capable of identifying a problem related to the program of study and carry out wholesome research on it leading to findings which will facilitate


development of a new/improved product, process for the benefit of the society. M.Tech projects should be socially relevant and research oriented ones. Each student is expected to do an individual project. The project work is carried out in two phases – Phase I in III semester and Phase II in IV semester. Phase II of the project work shall be in continuation of Phase I only. At the completion of a project the student will submit a project report, which will be evaluated (end semester assessment) by duly appointed examiner(s). This evaluation will be based on the project report and a viva voce examination on the project. The method of assessment for both Phase I and Phase II is shown in the following table:

Assessment Tool Weightage In- semester I review 10%

II review 15% III review 35%

End semester Final viva voce examination

40%

Student will be allowed to appear in the final viva voce examination only if he / she has submitted his / her project work in the form of paper for presentation / publication in a conference / journal and produced the proof of acknowledgement of receipt of paper from the organizers / publishers.


PROGRAM ELECTIVES

CS2104

DIGITAL IMAGE PROCESSING L T P C Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE The purpose of this course is to impart knowledge on various Digital Image Processing Techniques and their Applications INSTRUCTIONAL OBJECTIVES 1. To learn Image Fundamentals and Processing Techniques 2. To be familiar with Image Transformations in Spatial Domain and Frequency

Domain 3. To learn various Filters for Image Restoration 4. To study various Image Compression and Segmentation Techniques

UNIT I – DIGITAL IMAGE FUNDAMENTALS (8 hours) Introduction – Origin –Steps in Digital Image Processing – Components; Elements of Visual Perception – Light and Electromagnetic Spectrum – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels. UNIT II – IMAGE ENHANCEMENT (9 hours) Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–Smoothing and Sharpening Spatial Filtering – Frequency Domain: Introduction to Fourier Transform – Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters. UNIT III –IMAGE RESTORATION (9 hours) Noise models – Mean filters – Order Statistics – Adaptive filters – Band reject – Band pass – Notch – Optimum notch filtering – Inverse Filtering – Constrained Least Square Filtering – Wiener filtering. UNIT IV –IMAGE COMPRESSION (9 hours) Fundamentals – Image Compression models – Error Free Compression – Variable Length Coding –Bit – Plane Coding – Lossless Predictive Coding – Lossy Compression – Lossy Predictive Coding –Wavelet Coding – Compression Standards – JPEG2000. UNIT V – IMAGE SEGMENTATION AND REPRESENTATION (10 hours)


Segmentation – Detection of Discontinuities – Edge Linking and Boundary detection – Region based segmentation; Representation – Boundary descriptors – Simple Descriptors – Shape numbers –Regional descriptors – Simple and Topological Descriptors – Introduction to Image Processing Toolbox – Practice of Image Processing Toolbox – Case studies–Various Image Processing Techniques. REFERENCES 1. Rafael .C Gonzales, Richard E. Woods, “Digital Image Processing”, Pearson

Education, Third Edition, 2010. 2. Anil Jain .K, “Fundamentals of Digital Image Processing”, PHI Learning Pvt. Ltd.,

2011. 3. Jayaraman .S, Esaki Rajan S., T.Veera Kumar, “Digital Image Processing”, Tata

McGraw Hill Pvt. Ltd., Second Reprint, 2010. 4. Rafael .C, Gonzalez, Richard .E Woods, Steven L. Eddins, “Digital Image

Processing Using MATLAB”, Tata McGraw Hill Pvt. Ltd., Third Edition, 2011. 5. Bhabatosh Chanda, Dwejesh Dutta Majumder, “Digital Image Processing and

Analysis”, PHI Learning Pvt. Ltd., Second Edition, 2011. 6. Malay .K Pakhira, “Digital Image Processing and Pattern Recognition”, PHI

Learning Pvt. Ltd., First Edition, 2011. 7. Annadurai.S, Shanmugalakshmi.R, “Fundamentals of Digital Image Processing”,

Pearson Education, First Edition, 2007. 8. http://eeweb.poly.edu/~onur/lectures/lectures.html 9. http://www.caen.uiowa.edu/~dip/LECTURE/lecture.html

CS2108

PATTERN RECOGNITION TECHNIQUES L T P C Total Contact Hours – 45 3 0 0 3 Prerequisite Nill

PURPOSE To study the Pattern Recognition techniques and its applications. INSTRUCTIONAL OBJECTIVES 1. To learn the fundamentals of Pattern Recognition techniques. 2. To learn the various Statistical Pattern recognition techniques. 3. To learn the various Syntactical Pattern recognition techniques. 4. To learn the Neural Pattern recognition techniques.

http://eeweb.poly.edu/%7Eonur/lectures/lectures.html

http://www.caen.uiowa.edu/%7Edip/LECTURE/lecture.html


UNIT I – PATTERN RECOGNITION OVERVIEW (9 hours) Pattern recognition, Classification and Description—Patterns and feature Extraction with Examples—Training and Learning in PR systems—Pattern recognition Approaches UNIT II – STATISTICAL PATTERN RECOGNITION (9 hours) Introduction to statistical Pattern Recognition—supervised Learning using Parametric and Non Parametric Approaches. UNIT III – LINEAR DISCRIMINANT FUNCTIONS AND UNSUPERVISED LEARNING AND CLUSTERING (9 hours) Introduction—Discrete and binary Classification problems—Techniques to directly Obtain linear Classifiers -- Formulation of Unsupervised Learning Problems—Clustering for unsupervised learning and classification. UNIT IV – SYNTACTIC PATTERN RECOGNITION (9 hours) Overview of Syntactic Pattern Recognition—Syntactic recognition via parsing and other grammars–Graphical Approaches to syntactic pattern recognition—Learning via grammatical inference. UNIT V – NEURAL PATTERN RECOGNITION (9 hours) Introduction to Neural networks—Feedforward Networks and training by Back Propagation—Content Addressable Memory Approaches and Unsupervised Learning in Neural PR. REFERENCES 1. Robert Schalkoff, “Pattern Recognition: statistical , structural and neural

approaches”, John wiley & sons , Inc,1992. 2. Earl Gose, Richard johnsonbaugh, Steve Jost, “Pattern Recognition and Image

Analysis”, Prentice Hall of India,.Pvt Ltd, new Delhi, 1996 3. Duda R.O., Hart .P.E & D.G Stork, “ Pattern Classification”, 2nd Edition, J.Wiley

Inc 2001. 4. Duda .R.O & Hart .P.E, “Pattern Classification and Scene Analysis”, J.wiley Inc,

1973. 5. Bishop .C.M, “Neural Networks for Pattern Recognition”, Oxford University

Press, 1995.

CS2109 DATA WAREHOUSING AND ITS L T P C


APPLICATIONS Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE This course provides in-depth knowledge about data warehousing techniques INSTRUCTIONAL OBJECTIVES

1. To understand the fundamental concepts of data warehousing technology 2. To learn step-by-step approach to designing and building a data warehouse 3. To learn case-studies to bring out practical aspects of building a data

warehouse

UNIT I - INTRODUCTION TO DATA WAREHOUSING (8 hours) Introduction to data warehousing-data Warehouse: Defining features-Architecture of data warehouse-Gathering the business requirements. Planning and project management.

UNIT II - DATA WAREHOUSE SCHEMA (8 hours) Data Warehouse schema-Dimensional modeling-ETL Process-Testing, Growth and Maintenance-OLAP in the Data warehouse.

UNIT III - BUILDING A DATA WAREHOUSE (10 hours) Building a data warehouse-Introduction-critical success factors-Requirement analysis-Planning for the data warehouse-The data warehouse design stage-Building and implementing data marts-Building data warehouses-backup and Recovery-Establish the data quality framework-Operating the Warehouse-Recipe for a successful warehouse-Data warehouse pitfalls.

UNIT IV - DATA MINING BASICS (8 hours) Data Mining basics-Moving into data mining-Introduction to Web Mining, Text Mining Temporal Data Mining and Spatial Data mining-Issues in Data Mining.

UNIT V - CASE STUDY (11 hours) Data Warehousing in the Tamilnadu Government-Data Warehouse for the Ministry of commerce- Data Warehouse for the government of Andhra Pradesh- Data Warehousing in Hewlett –Packard- Data Warehousing in Levi Strauss- Data Warehousing in the World Bank-HARBOR, A Highly available Data Warehouse-A typical Business data Warehouse for a Trading company-Customer data warehouse of the world’s first and largest online Bank in the united Kingdom-A German super market EDEKA’s Data Warehouse. REFERENCES 1. Reema Theraja “Data Warehousing” by Oxford University Press-2011.


2. Prabhu .C.S.R, “Data Warehousing Concepts, Techniques, Products and Applications” PHI Learning Private Limited, Third Edition, 2011.

3. Amitesh Sinha,. “Data Warehousing”, Thomson Asia Pte Ltd-2001.

CS2121

MACHINE VISION L T P C Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE The purpose of this course is to impart knowledge on various elements and techniques of machine vision and to expose the students to its practical applications INSTRUCTIONAL OBJECTIVES

1. To learn about Image formation concepts in computers 2. To be familiar with earlier approaches of machine vision 3. To explore the middle level approaches in machine vision 4. To learn about high level techniques used in machine vision 5. To have an outlook on some practical applications of machine vision

techniques

UNIT I - IMAGE FORMATION (9 hours) Image formation Introduction-Cameras with lenses-Modelling pixel brightness-Radio metric calibration and high dynamic range Images-photometric stereo-human color perception-color representation-Inference from color

UNIT II - EARLY VISION (9 hours) Linear filters and convolution-Edge effects in discrete convolution-Sampling and aliasing-Applications of scaled representations-Computing image gradients-gradient based edge detection-neighbourhoods with SIFT and HOG features-local texture representations using filters-Image denoising.

UNIT III - MIDDLE LEVEL VISION (9 hours) Image segmentation by clustering pixels-Watershed algorithm-segmentation using K-means-fitting lines with Hough transform-RANSAC-mixture models-flow models-tracking by detection-tracking with kalman filter.

UNIT IV - HIGH LEVEL VISION (9 hours) Registering rigid objects-Koenderink’s theorem –local and multi local visual events-object recognition using Spin images-decision trees and random forests-classification and its strategies (histogram, naïve bayes, ada boost and SVM)-detecting objects using slide window.

UNIT V - APPLICATIONS (9 hours) Image based modeling and rendering of Visual Hulls-patch based multi view Stereopsis –ranking documents-tracking people-3D from 2D.

REFERENCES


1. David Forsyth and Jean Ponce , “Computer Vision – A modern approach” 2nd edition, Prentice Hall, 2011.

2. Milan Sonka, Vaclav Hlavac and Roger Boyle, “Image Processing, Analysis and Machine Vision”, Thomson, 2007.

CS2123

HUMAN INTERFACE SYSTEM DESIGN L T P C Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE This course on user Interface Design provides a basic understanding of interface design and principles. INSTRUCTIONAL OBJECTIVES 1. To Learn the basic fundamentals of the HISD 2. To Learn the various aspects of managing the human interface design 3. To Understand the various aspects involved in virtual environment and manipulation 4. To be familiar with various interfaces available UNIT I - INTRODUCTION (9 hours) Goals of System Engineering - Goals of User Interface Design - Motivations of Human factors in Design - High Level Theories -Object - Action Interface Design - Three Principles - Guidelines for Data Display and Data Entry UNIT II - MANAGING DESIGN PROCESS (9 hours) Introduction - Examples of Direct Manipulation Systems -Explanation of Direct Manipulation - Visual Thinking and Icons - Direct manipulation Programming - Home Automation- Remote Direct Manipulation- Virtual Environments - Task - Related Organization - Item Presentation Sequence- Response Time and Display Rate - Fast Movement Through Menus- Menu Layouts- Form Fillin - Dialog Box - Functionality to Support User's Tasks - Command Organization Strategies - Benefits of Structure- Naming and Abbreviations - Command Menus- Natural Language in Computing. UNIT III - MANIPULATION AND VIRTUAL ENVIRONMENTS (9 hours) Introduction - Examples of Direct Manipulation Systems -Explanation of Direct Manipulation - Visual Thinking and Icons - Direct manipulation Programming - Home Automation- Remote Direct Manipulation- Virtual Environments - Task - Related Organization - Item Presentation Sequence- Response Time and Display Rate - Fast Movement Through Menus- Menu Layouts- Form Fillin - Dialog Box - Functionality to Support User's Tasks - Command Organization Strategies - Benefits of Structure- Naming and Abbreviations - Command Menus- Natural Language in Computing.


UNIT IV - INTERACTION DEVICES (9 hours) Introduction – Keyboards and Functions – Pointing Devices- Speech recognition ,Digitization and Generation – Image and Video Displays – Printers –Theoretical Foundations –Expectations and Attitudes – User Productivity – Variability – Error messages – Nonanthropomorphic Design –Display Design – color-Reading from Paper versus from Displays- Preparation of Printed Manuals- Preparation of Online Facilities. UNIT V - WINDOWS STRATEGIES AND INFORMATION SEARCH (9 hours) Introduction- Individual Widow Design- Multiple Window Design- Coordination by Tightly -Coupled Widow- Image Browsing- Personal Role Management and Elastic Windows - Goals of Cooperation - Asynchronous Interaction - Synchronous Distributed - Face to Face- Applying Computer Supported Cooperative Work to Education - Database query and phrase search in Textual documents - Multimedia Documents Searches - Information Visualization - Advance Filtering Hypertext and Hypermedia - World Wide Web- Genres and Goals and Designers - Users and their tasks - Object Action Interface Model for Web site Design. REFERENCES 1. Ben Shneiderman .J, "Designing the User Interface", 3rd Edition, Addison -

Wesley, 2001. 2. Wilbert .O Galiz, "The Essential guide to User Interface Design", Wiley

Dreamtech, 2002. 3. Jacob Nielsen, "Usability Engineering ", Academic Press, 1993.


CS2124

REASONING UNDER UNCERTAINTY L T P C Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE This course presents a detailed knowledge of Reasoning Under Uncertainty and various models to represent it. INSTRUCTIONAL OBJECTIVES 1. To understand Reasoning Under Uncertainty 2. To understand various models to represent Reasoning Under uncertainty 3. To understand various inference mechanisms in Reasoning Under uncertainty UNIT I - INTRODUCTION (9 hours) Motivation and background - Qualitative and quantitative approaches to reasoning under uncertainty - Probabilistic reasoning- Probabilistic representation of uncertainty - Probability distributions-Prior and conditional probability-Inference using joint distributions-Conditional independence and Bayes' rule - The semantic of Bayesian networks-Conditional independence relations - Efficient representation of conditional distributions. UNIT II - DEPENDENCY MODELS AND MAPS (9 hours) Qualitative reasoning about independence relationships-Dependency models and dependency maps- Bayesian networks- The semantic of Bayesian networks- Efficient representation of conditional distributions-Reasoning with Bayesian networks. UNIT III - INFERENCE IN BNS (9 hours) The complexity of exact inference-Inference by enumeration-Pearl's message passing algorithm-The variable elimination algorithm-Clustering methods - Junction trees- Approximate inference in BNs- Approximate inference with stochastic simulation-Direct sampling methods, rejection sampling and likelihood weighting-Markov Chain Monte Carlo (MCMC). UNIT IV - CAUSAL INFERENCE (9 hours) Reasoning about cause and effect Causes and explanations- Decision networks - The basis of utility theory-Decision trees and influence diagrams-The value of perfect and imperfect information-Evaluating influence diagrams. UNIT V - PROBABILISTIC REASONING OVER TIME (9 hours)


Inference in temporal models -Dynamic Bayesian networks-Inference algorithms for DBNs- Applications of BNs and knowledge engineering - Knowledge engineering with BNs -Evaluation and validation methods.

REFERENCES 1. Stuart Russel and Peter Norvig , “Artificial Intelligence: A Modern Approach”,

Pearson Education 3rd Edition, 2012. 2. Dimitri P. Bertsekas and John N. Tsitsikli ,” Introduction to Probability”, 2nd

Edition. S Athena Scientific. 2008. 3. Jonathan Baron, “Thinking and Deciding”, 4th Edition.Cambridge University

Press 2007.

CS2125

SPEECH AND LANGUAGE PROCESSING L T P C Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE To expose the students to the basic principles of speech and language processing and typical applications of natural language processing systems INSTRUCTIONAL OBJECTIVES 1. To provide a general introduction including the use of state automata for language

processing 2. To provide the fundamentals of syntax including a basic parse 3. To explain advanced feature like feature structures and realistic parsing

methodologies 4. To give details about a typical natural language processing applications UNIT I – INTRODUCTION (9 hours) Knowledge in speech and language processing - Ambiguity - Models and Algorithms - Language, Thought and Understanding. Regular Expressions and automata: Regular expressions - Finite-State automata. Morphology and Finite-State Transducers: Survey of English morphology - Finite-State Morphological parsing - Combining FST lexicon and rules - Lexicon-Free FSTs: The porter stammer - Human morphological processing.


UNIT II – SYNTAX (9 hours) Word classes and part-of-speech tagging: English word classes - Tagsets for English - Part-of-speech tagging - Rule-based part-of-speech tagging - Stochastic part-of-speech tagging - Transformation-based tagging - Other issues. Context-Free Grammars for English: Constituency - Context-Free rules and trees - Sentence-level constructions - The noun phrase - Coordination - Agreement - The verb phase and sub categorization - Auxiliaries - Spoken language syntax - Grammars equivalence and normal form - Finite-State and Context-Free grammars - Grammars and human processing. Parsing with Context-Free Grammars: Parsing as search - A Basic Top-Down parser - Problems with the basic Top-Down parser - The early algorithm - Finite-State parsing methods.

UNIT III – ADVANCED FEATURES AND SYNTAX (9 hours) Features and Unification: Feature structures - Unification of feature structures - Features structures in the grammar - Implementing unification - Parsing with unification constraints - Types and Inheritance. Lexicalized and Probabilistic Parsing: Probabilistic context-free grammar - problems with PCFGs - Probabilistic lexicalized CFGs - Dependency Grammars - Human parsing.

UNIT IV – SEMANTIC (9 hours) Representing Meaning: Computational desiderata for representations - Meaning structure of language - First order predicate calculus - Some linguistically relevant concepts - Related representational approaches - Alternative approaches to meaning. Semantic Analysis: Syntax-Driven semantic analysis - Attachments for a fragment of English - Integrating semantic analysis into the early parser - Idioms and compositionality - Robust semantic analysis. Lexical semantics: relational among lexemes and their senses - WordNet: A database of lexical relations - The Internal structure of words - Creativity and the lexicon.

UNIT V – APPLICATIONS (9 hours) Word Sense Disambiguation and Information Retrieval: Selectional restriction-based disambiguation - Robust word sense disambiguation - Information retrieval - other information retrieval tasks. Natural Language Generation: Introduction to language generation - Architecture for generation - Surface realization - Discourse planning - Other issues. Machine Translation: Language similarities and differences - The transfer metaphor - The interlingua idea: Using meaning - Direct translation - Using statistical techniques - Usability and system development.

REFERENCES 1. Daniel Jurafsky & James .H Martin, "Speech and Language Processing",

Pearson Education (Singapore) Pvt. Ltd., 2002. 2. James Allen, "Natural Language Understanding", Pearson Education, 2003.

CS2126 DEDUCTIVE AND INDUCTIVE REASONING L T P C


Total Contact Hours – 45 3 0 0 3 Prerequisite Nill

PURPOSE This course presents a detailed knowledge of the principles of deductive and inductive reasoning, fallacies and their applications. INSTRUCTIONAL OBJECTIVES 1. To understand the definitions and approaches to deductive reasoning. 2. To comprehend the inductive methods and fallacies. 3. To learn the applications of inductive and deductive reasoning.

UNIT I – FORMAL LANGUAGE CONCEPTS (9 hours) Some definitions of formal logical concepts- Classical symbolic logic – symbolic representation of language statements – formal logical rules of inference – semantics in formal logic – provability relation – does formal logic model

UNIT II – HUMAN REASONING (9 hours) Human reasoning – mental model theory – revised model theory of conditionals - nonmonotonic logic - categorization and default reasoning – minimal model semantics-default entailment relation – some characteristic of belief - biases in human reasoning .

UNIT III – HEURISTICS (9 hours) The representativeness heuristics and the availability heuristics – Atmosphere effect – effects of negation - Introduction – Affirming the consequent and denying the antecedent – errors in the interpretation of standard form categorical propositions – fallacies due to ambiguity of language - language nuances associated with conditional statements – conditional inferences made of ‘only if’statements - ordinary languages Vs formal language definitions of quantifiers.

UNIT IV – INDUCTIVE INFERENCE (9 hours) Nature of Inductive inference – method of agreement – method of difference – method of residues – method of concomitant variations – argument from analogy – Imperfect applications of Inductive methods – relation of induction to deduction and verification.

UNIT V - FALLACIES OF INDUCTIVE REASONING (9 hours) Fallacies of generalization – Fallacies of non-observation – False analogy – interpreting asymmetries of projection in Children’s inductive reasoning - use of single or multiple categories in category based induction – abductive inference from philosophical analysis to neural mechanisms. REFERENCES 1. Thomas Fowler “Logic:Deductive and Inductive”, Adamant Media Corporation

2004.


2. Aidan Feeney and Evan Heit, “Inductive Reasoning: Experimental, developmental and computational approaches”, Cambridge University Press, 2007.

CS2127

BIOINFORMATICS L T P C Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE This course provides an overview of bioinformatics from a computer science perspective which makes the computer science aspects of bioinformatics more understandable for life scientists. INSTRUCTIONAL OBJECTIVES 1. To understand Fundamental concepts and state-of-the-art tools 2. To learn about very large biological databases: object-oriented database methods,

data mining/warehousing and knowledge management. 3. To explore the inner workings of biological structures. 4. To study advanced pattern matching techniques, including microarray research

and gene prediction.

UNIT I -INTRODUCTORY CONCEPTS (9 hours) The Central Dogma – The Killer Application – Parallel Universes – Watson’s Definition – Top Down Versus Bottom up – Information Flow – Convergence – Databases – Data Management – Data Life Cycle – DatabaseTechnology – Interfaces – Implementation – Networks – Geographical Scope – Communication Models – Transmissions Technology – Protocols – Bandwidth – Topology – Hardware – Contents – Security – Ownership – Implementation – Management.

UNIT II -SEARCH ENGINES AND DATA VISUALIZATION (9 hours) The search process – Search Engine Technology – Searching and Information Theory – Computational methods – Search Engines and Knowledge Management – Data Visualization – sequence visualization – structure visualization – user Interface –Animation Versus simulation – General Purpose Technologies.


UNIT III -STATISTICS AND DATA MINING (9 hours) Statistical concepts – Microarrays – Imperfect Data– Randomness – Variability – Approximation – Interface Noise – Assumptions – Sampling and Distributions –Hypothesis Testing – Quantifying Randomness – Data Analysis – Tool selection statistics of Alignment – Clustering and Classification – Data Mining – Methods – Selection and Sampling – Preprocessing and Cleaning– Transformation and Reduction – Data Mining Methods – Evaluation – Visualization – Designing new queries – Pattern Recognition and Discovery – Machine Learning – TextMining – Tools.

UNIT IV - PATTERN MATCHING (9 hours) Pairwise sequence alignment – Local versus global alignment – Multiple sequence alignment – Computational methods – Dot Matrix analysis – Substitution matrices –Dynamic Programming – Word methods – Bayesian methods – Multiple sequence alignment – Dynamic Programming – Progressive strategies – Iterative strategies – Tools – Nucleotide Pattern Matching – Polypeptide pattern matching – Utilities –Sequence Databases.

UNIT V - MODELING AND SIMULATION (9 hours) Drug Discovery – components – process – Perspectives – Numeric considerations – Algorithms – Hardware – Issues – Protein structure – AbInitio Methods – Heuristic methods – Systems Biology – Tools – Collaboration and Communications – standards - Issues – Security – Intellectual property.

REFERENCES 1. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson

Education, 2003. 2. Attwood .T.K and Perry Smith .D.J, “Introduction to Bio Informatics”, Longman

Essen, 1999.

CS2128

SPATIO TEMPORAL REASONING L T P C Total Contact Hours - 45 3 0 0 3 Prerequisite Nill

PURPOSE This course presents a detailed knowledge of Spatial and temporal based reasoning techniques and theirapplications INSTRUCTIONAL OBJECTIVES 1. To understand Spatial reasoning and representations 2. To understand temporal problems and solutions 3. To understand applications of spatio-temporal reasoning

UNIT - I SPATIAL REPRESENTATION (9 hours)


Aspects of Spatial Representation - What is knowledge representation – what is so special about spatial – Qualitative, quantitative and hybrid approaches – frame of reference – points vs. extended objects- Points of view on spatial relations – granularity and vagueness – overview of extant approaches.

UNIT - II TOPOLOGY AND APPLICATIONS OF GIS (9 hours) Topology – orientation –size and distance – shape - Reasoning With Spatial Representations - Role of domain structure – transforming frames of reference – composition of spatial relations – topological relations – orientation – distance – constraint propagation and relaxation- Applications – GIS – Conceptual design in 2D and 3D – emerging trends and technologies.

UNIT - III TEMPORAL PROBLEMS AND ALGORITHMS (9 hours) Simple Temporal Problem - Problem representations and solutions – minimal network –Complexity – solution techniques – Floyd’s and Warshall’s algorithm – Bellman and Ford’s algorithm – Johnson’s algorithm – directed path consistency – partial path consistency.

UNIT - IV TCSP, DTP AND ALGORITHMS (9 hours) TCSP AND DTP - Examples – Definition – the temporal constraint satisfaction problem – The disjunctive temporal problem – object level and meta level – Complexity – Preprocessing – path consistency – upper lower tightening – loose path consistency -Solving TCSP – standard backtracking – improvements - solving DTP - Stergiou’s and Koubarakis’ algorithm. UNIT – V SPATIAL INFORMATION SYSTEMS (9 hours) Improvements Applications - A generic model for spatio-bi-temporal geographic Information – process dynamics, temporal extent and casual propagation as the basis for linking space and time – relationship between geographic scale,distance and time as expressed in natural discourse – acquiring spatio-temporal knowledge from language – analyzing temporal factors in urban morphology development-The cognitive atlas – using GIS as a metaphor for Memory. REFERENCES 1. Max .J Egenhofer and Reginald .G Golledge, “Spatial and Temporal reasoning in

Geographic InformationSystems”, Oxford University Press, 1998. 2. Michael Fisher Ed, Dov Gabbay M, Lluis Vila, “ Handbook of Temporal reasoning

in Artificial Intelligence”, Springer, 2005. 3. Daniel Hernandez and Amitava Mukherjee, Leon Planken, “Reference Notes”.


SUPPORTIVE COURSES

MA2013

MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE L T P C

Total Contact Hours – 45 3 0 0 3 Prerequisite Nill

PURPOSE To impart analytical ability and to solve real life problems pertaining to branches of Computer Science and Engineering. INSTRUCTIONAL OBJECTIVES

1. To be exposed with logic. 2. To be thorough in mathematical induction. 3. To understand algebraic systems such as relations. 4. To be familiar with the basic concepts of lattices.

UNIT I – LOGIC (9 hours) Logic - Statements - Connectives - Truth tables - Normal forms - Predicate calculus - Inference Theory for Statement calculus and predicate calculus. UNIT II – COMBINATORICS (9 hours) Combinatory - Mathematical Induction - Pigeonhole principle - Principle of inclusion and exclusion. UNIT III- RECURSIVE FUNCTIONS (9 hours) Recursive Functions- Recurrence relation - Solution of recurrence relation using characteristic polynomial and using generating function - Recursive functions - Primitive recursive functions, Computable and non computable functions. UNIT IV- ALGEBRAIC STRUCTURES (9 hours) Algebraic Structures - Groups - Definition and examples only - Cyclic groups - Permutation group (Sn and Dn) - Subgroups - Homomorphism and Isomorphism - Cosets - Lagrange's Theorem - Normal subgroups - Cayley's representation theorem. UNIT V – LATTICES (9 hours) Lattices - Partial order relations, Poset - Lattices, Hasse diagram - Boolean algebra. REFERENCES 1. Tremblay .J.P and Manohar .R, "Discrete Mathematical Structures with

applications to Computer Science", McGraw Hill International Edition, 1987.


2. Kenneth .H Rosen, “Discrete Mathematics and Its Applications”, 4th Edition, Tata McGraw Hill, 2002.

3. Venkataraman M.K. etal., "Discrete Mathematics", National Publishing Co.,2000. 4. Prof. V. Sundaresan, K.S. Ganapathy Subramanian and K. Ganesan, “Discrete

Mathematics”, New Revised Edition, 2001. 5. Alan Doerr and Kenneth Levasseur, "Applied Discrete Structures for Computer

Science", Galgotia Publications (P) Ltd.,1992. 6. Liu .C.L, “Elements of Discrete Mathematics”, 2nd Edition, McGraw Hill

Publications, 1985. 7. Gersting .J.L , “Mathematical Structures for Computer Science”, 3rd Edition,

W.H. Freeman and Co., 1993. 8. Lidl and Pitz, “Applied abstract Algebra”, Springer - Verlag, New York, 1984.

MA2010

GRAPH THEORY AND OPTIMIZATION TECHNIQUES L T P C


PURPOSE To develop analytical capability and to impart knowledge in graphs, linear programming problem and statistical methods and their applications in Engineering & Technology and to apply their concepts in engineering problems they would come across INSTRUCTIONAL OBJECTIVES

1. student should be able to understand graphs ,linear programming problems and statistical concepts.

2. Students should be able to apply the concepts in solving the Engineering problems

UNIT I - BASICS OF GRAPH THEORY (9 hours) Graphs - Data structures for graphs - Subgraphs - Operations on Graphs Connectivity – Networks and the maximum flow - Minimum cut theorem - Trees - Spanning trees - Rooted trees – Matrix representation of graphs. UNIT II - CLASSES OF GRAPHS (9 hours) Eulerian graphs and Hamiltonian graphs - Standard theorems - Planar graphs - Euler's formula - Five colour theorem - Coloring of graphs - Chromatic number (vertex and edge) properties and examples - Directed graphs. UNIT III- GRAPH ALGORITHM (9 hours)


Computer Representation of graphs - Basic graph algorithms - Minimal spanning tree algorithm - Kruskal and Prim's algorithm - Shortest path algorithms - Dijsktra's algorithm - DFS and BFS algorithms. UNIT IV - OPTIMIZATION TECHNIQUES (9 hours) Linear programming – Graphical methods – Simplex method (Artificial variables not included) – Transportation and assignment problems. UNIT V – STATISTICS (9 hours) Tchebyshev’s inequality – Maximum likelihood estimation – Correlation – Partial correlation – Multiple correlations. REFERENCES 1. Narsingh Deo, “Graph Theory with Applications to Engineering and Computer

Science”, PHI,1974. 2. Rao .S.S. “Engineering Optimization: Theory and Practice”, New Age

International Pvt. Ltd., 3rd Edition1998.

MA2011

STOCHASTIC PROCESSES & QUEUEING THEORY L T P C


PURPOSE To impart knowledge on probability concepts to study their applications in stochastic processes & queueing theory INSTRUCTIONAL OBJECTIVES 1. Compute the characteristics of the random variable given the probabilities 2. Understand and apply various distribution 3. Solve cases of different Stochastic processes along with their properties. 4. Use discrete time finite state Markov chains 5. Gain sufficient knowledge in principles of queueing theory UNIT I - RANDOM VARIABLES (9 hours) One dimensional and two dimensional Random Variables – Characteristics of Random Variables : Expectation, Moments. UNIT II- THEORETICAL DISTRIBUTIONS (9 hours) Discrete : Binomial, Poisson, Negative Binomial, Geometric, Uniform Distributions. Continuous: Uniform, Exponential, Erlang and Gamma, Weibull Distributions.


UNIT III - STOCHASTIC PROCESSES (9 hours) Classification of Stochastic Processes – Bernoulli process – Poisson process – Pure birth process – Birth and Death process. UNIT IV - MARKOV CHAINS (9 hours) Introduction – Discrete-Parameter Markov Chains – Transition Probability Matrix – Chapman Kolmogorov Theorem – State classification and limiting distributions. UNIT V - QUEUING THEORY (9 hours) Introduction – Characteristics of Markovian Single server and Multi server queuing models [(M/M/1) : (∞ / FIFO), (M/M/1) : (N / FIFO), (M/M/s) : (∞ /FIFO)] – M/G/1 Queuing System – Pollaczek Khinchin formula. REFERENCES 1. Kishore.S.Trivedi, “Probability & Statistics with Reliability, Queuing and

Computer Science Applications”, PHI, New Delhi, 1995. 2. Veerajan T, “Probability, Statistics and Random Processes”, 3rd Edition Tata

McGraw Hill, New Delhi, 2008. 3. Gupta S.C and Kapoor V.K, “Fundamentals of Mathematical Statistics”, 9th

revised edition, Sultan Chand & Co., New Delhi 2003. 4. Gross.D and Harris.C.M. “Fundementals of Queuing theory”, John Wiley and

Sons, 1985. 5. Allen.A.O, “Probability, Statistics and Queuing Theory”, Academic Press, 1981.

SEMESTER I

CAC2001

Career Advancement Course For Engineers - I

L T P C


PURPOSE To enhance holistic development of students and improve their employability skills

INSTRUCTIONAL OBJECTIVES 1. To improve aptitude, problem solving skills and reasoning ability of the student. 2. To collectively solve problems in teams & group.


3. Understand the importance of verbal and written communication in the workplace 4. Understand the significance of oral presentations, and when they may be used. 5. Practice verbal communication by making a technical presentation to the class 6. Develop time management Skills UNIT I–BASIC NUMERACY

Types and Properties of Numbers, LCM, GCD, Fractions and decimals, Surds UNIT II-ARITHMETIC – I

Percentages, Profit & Loss, Equations UNIT III-REASONING - I

Logical Reasoning

UNIT IV-SOFT SKILLS - I Presentation skills, E-mail Etiquette

UNIT V-SOFT SKILLS - II

Goal Setting and Prioritizing

ASSESSMENT Soft Skills (Internal) Assessment of presentation and writing skills. Quantitative Aptitude (External) Objective Questions- 60 marks Descriptive case lets- 40 marks* Duration: 3 hours *Engineering problems will be given as descriptive case lets. REFERENCE: 1. Quantitative Aptitude by Dinesh Khattar – Pearsons Publicaitons 2. Quantitative Aptitude and Reasoning by RV Praveen – EEE Publications

3. Quantitative Aptitude by Abijith Guha – TATA Mc GRAW Hill Publications 4. Soft Skills for Everyone by Jeff Butterfield – Cengage Learning India Private Limited 5. Six Thinking Hats is a book by Edward de Bono - Little Brown and Company 6. IBPS PO - CWE Success Master by Arihant - Arihant Publications(I) Pvt.Ltd – Meerut

SEMESTER II

CAC2002

Career Advancement Course For Engineers - II

L T P C


PURPOSE To enhance holistic development of students and improve their employability skills

INSTRUCTIONAL OBJECTIVES 1. To improve aptitude, problem solving skills and reasoning ability of the student. 2. To collectively solve problems in teams & group. 3. Understand the importance of verbal communication in the workplace 4. Understand the significance of oral presentations, and when they may be used. 5. Understand the fundamentals of listening and how one can present in a group discussion 6. Prepare or update resume according to the tips presented in class. UNIT I-ARITHMETIC – II

Ratios & Proportions, Mixtures & Solutions

UNIT II - MODERN MATHEMATICS Sets & Functions, Data Interpretation, Data Sufficiency

UNIT III – REASONING - II

Analytical Reasoning


http://en.wikipedia.org/wiki/Edward_de_Bono


UNIT IV – COMMUNICATION - I

Group discussion, Personal interview UNIT V - COMMUNICATION - II

Verbal Reasoning test papers ASSESSMENT Communication (Internal)

• Individuals are put through formal GD and personal interviews. • Comprehensive assessment of individuals’ performance in GD & PI

will be carried out. Quantitative Aptitude (External) Objective Questions- 60 marks (30 Verbal +30 Quants) Descriptive case lets- 40 marks* Duration: 3 hours *Engineering problems will be given as descriptive case lets. REFERENCES 1. Quantitative Aptitude by Dinesh Khattar – Pearsons Publicaitons 2. Quantitative Aptitude and Reasoning by RV Praveen – EEE Publications 3. Quantitative Aptitude by Abijith Guha – TATA Mc GRAW Hill Publications 4. General English for Competitive Examination by A.P. Bharadwaj – Pearson Educaiton 5. English for Competitive Examination by Showick Thorpe - Pearson Educaiton 6. IBPS PO - CWE Success Master by Arihant - Arihant Publications(I) Pvt.Ltd - Meerut 7. Verbal Ability for CAT by Sujith Kumar - Pearson India 8. Verbal Ability & Reading Comprehension by Arun Sharma - Tata McGraw - Hill Education


SEMESTER III

CAC2003

Career Advancement Course For Engineers - III

L T P C


PURPOSE

To develop professional skills abreast with contemporary teaching learning methodologies INSTRUCTIONAL OBJECTIVES At the end of the course the student will be able to 1 acquire knowledge on planning, preparing and designing a learning

program

2 prepare effective learning resources for active practice sessions 3 facilitate active learning with new methodologies and approaches 4 create balanced assessment tools 5 hone teaching skills for further enrichment UNIT I- DESIGN (2 hrs)

Planning &Preparing a learning program. Planning & Preparing a learning session

UNIT II – PRACTICE (2 hrs)

Facilitating active learning Engaging learners

UNIT III – ASSESSMENT (2 hrs)

Assessing learner’s progress Assessing learner’s achievement

UNIT IV – HANDS ON TRAINING (10 hrs)

Group activities – designing learning session Designing teaching learning resources Designing assessment tools Mock teaching session


UNIT V – TEACHING IN ACTION (14 hrs) Live teaching sessions Assessments

ASSESSMENT (Internal)

Weightage:

Design - 40% Practice – 40% Quiz – 10% Assessment – 10%

REFERENCES

Cambridge International Diploma for Teachers and Trainers Text book by Ian Barker - Foundation books Whitehead, Creating a Living Educational Theory from Questions of the kind: How do I improve my Practice? Cambridge J. of Education


AMENDMENTS

S.No. Details of Amendment Effective from Approval with date