SRI VENKATESWARA UNIVERSITY: TIRUPATI – 517 502

2-Year M.Tech (CSE)

Choice Based Credit System (CBCS)

(With effect from the academic year 2008-09)

List of Electives

MCS 501                                                                                                                         TOP

SRI VENKATESWARA UNIVERSITY :: TIRUPATI

M.Tech (CSE) - (CBCS)

(With effect from the academic year 2008 – 09)

MACHINE LEARNING

No. of Credits: 4

UNIT I

Introduction: Concept of machine learning, Applications – Learning associations, Classification, Regression, Unsupervised learning, Reinforcement learning.

Supervised Learning: Learning a class from examples, VC dimension, PAC learning, Learning multiple classes, Regression, Model selection, and generalization.

Bayesian Decision Theory: Classification, Losses and risks, Discriminant functions, Utility theory, Value of information, Bayesian networks, Influence diagrams, Association rules.

UNIT II

Parametric Methods: Maximum likelihood estimation, Evaluating an estimator, The Bayes’ estimator, Parametric classification, Regression, Tuning model complexity, Model selection procedures.

Multivariate Methods: Parameter estimation, Estimation of missing values, Multivariate normal distribution, Multivariate classification, Tuning complexity, Discrete features, Multivariate regression.

Dimensionality Reduction: Subset selection, Principal components analysis, Factor analysis, Multidimensional scaling, Linear discriminant analysis.

UNIT III

Clustering: Mixture densities, k-means clustering, Expectation-maximization algorithm, Hierarchical clustering.

Nonparametric Methods: Nonparametric density estimation, Generalization to multivariate data, Nonparametric classification, Condensed nearest neighbor, Nonparametric regression – smoothing models.

Decision Trees: Univariate trees, Pruning, Rule extraction from trees, Learning rules from data, Multivariate trees.

UNIT IV

Linear Discrimination: Geometry of linear discriminant, Pair wise separation, Gradient descent, Logistic discrimination, Discrimination by regression, Support vector machines – Optimal separating hyper plane, Soft margin hyper plane, kernel functions.

Hidden Markov Models: Discrete Markov processes, Hidden Markov models, Basic problems of HMMs – Evaluation, Finding the state sequence, Learning model parameters;  The HMM with input, Model selection in HMM.

UNIT V

Assessing and Comparing Classification Algorithms:  Cross-validation and resampling methods, Measuring error, Interval estimation, Hypothesis testing, Assessing  performance of classification algorithms – Binomial test, Approximate normal test, Paired t test;  Comparing classification algorithms – McNemar’s test, k-fold cross- validated paired t test, 5x2 cv paired t test, 5x2 cv paired F test, Anova.

Combining Multiple Learners: Justification, Voting, Error-correcting output codes, Bagging, Boosting, Mixture of experts, Stacked generalization, Cascading.

Reinforcement Learning: Single state case – K-armed bandit, Elements of reinforcement learning, Model-based learning, Temporal difference learning, Generalization, Partially observable states.

Text Books:

1.            Alpaydin E, Introduction to Machine Learning, Prentice-Hall of India Pvt. Ltd., 2004.

2.            Mitchell T M, Machine Learning, The McGraw-Hill Companies, Inc., 1997.

Reference Books:

1.            Russel S, and Norvig P, Artificial Intelligence: A Modern Approach, 2nd Edition, Prentice-Hall, 2003.

2.            Duda R, Hart P, and Stork D, Pattern Classification, Wiley, 2001.

3.            Kearns M, and Vazirani U, Computational Learning Theory, MIT Press, 1994. 

MCS 502                                                                                                                         TOP

SRI VENKATESWARA UNIVERSITY :: TIRUPATI

M.Tech (CSE) - (CBCS)

(With effect from the academic year 2008 – 09)

SOFTWARE ARCHITECTURES

No. of Credits: 4

UNIT I

Introduction, Architectural styles – Pipes and filters, Data abstraction and object-oriented organization, Event-based implicit invocation, Layered systems, Repositories, Interpreters, Process control, Other familiar architectures, Heterogeneous architectures.

Shared information systems – Database integration, Integration in software development environments, Integration in the design of buildings, Architectures for shared information systems.

UNIT II

Architectural design guidance – Guidance for user-interface architecture, Quantified design space.

Formal models and specification – Value of architectural formalism, Formalizing architecture of a specific system, Formalizing an architectural style, Formalizing an architectural design space, Toward a theory of software architecture.

UNIT III

Architectural Patterns – Introduction, From mud to structure, Distributed systems, Interactive systems, Adaptable systems.

UNIT IV

Design  Patterns – Introduction, Structural decomposition, Organization of work, Access control, Management, Communication

UNIT V

Idioms – Introduction, What can idioms provide?  Idioms and style, Where can you find idioms,

Patterns and Software Architecture – Introduction, Patterns in software architectures, Enabling techniques for software architecture, Non-functional properties of software architecture.

Text Books:

1.            Shaw M and Garlan D, Software Architecture- Perspectives on an Emerging Discipline, Prentice-Hall of India, 2004. ( Chapters 1, 2 and 4 for Unit I;   5 and 6 for Unit II)

2.             Buschmann F, Meunier R, Rohnert H, Sommerlad P, and Stal M, Pattern-Oriented Software architecture : A system of patterns – Vol.1, John-Wiley, 2001. ( Chapters 1 and 2 for Unit III;  3 for Unit 4; 4 and 6 for Unit V)

Reference Books:

1.            Bass L, Clements P, and Kazman R, Software Architecture in Practice, 2^nd Edition, Pearson Education, 2005.

2.             Gamma E, Helm R, Johnson R, and Vlissides J, Design Pattern: Elements of Reusable Object-Oriented Software, Pearson Education, 2005.

MCS 503                                                                                                                         TOP

SRI VENKATESWARA UNIVERSITY :: TIRUPATI

M.Tech (CSE) - (CBCS)

(With effect from the academic year 2008 – 09)

DISTRIBUTED COMPUTING

No. of Credits: 4

UNIT I

Introduction, Model of a computation, Logical clocks, Clocks of different dimensions, Mutual exclusion - Using timestamps, Tokens and quorums.

UNIT II

Drinking philosophers problem, Leader election, Global state, Observing global predicates, Observing conjunctive predicates.

UNIT III

Control of distributed computation, Causal message ordering, Synchronous and total order, Computation of a global function, Repeated global computation.

UNIT IV

Synchronizers, Slicers, Distributed shared memory, Self-stabilization, Knowledge and common knowledge.

UNIT V

Consensus under asynchrony, Consensus under synchrony, Failure detectors, Solvable problems in asynchronous systems, Check-pointing for recovery, Message logging for recovery.

Text Books:

      Garg V K, Elements of Distributed Computing, Wiley, 2002. (All chapters except 4, 13, and 14)

Reference Book:

      Emmerich W, Engineering Distributed Objects, Wiley, 2000.

MCS 504                                                                                                                         TOP

SRI VENKATESWARA UNIVERSITY :: TIRUPATI

M.Tech (CSE) - (CBCS)

(With effect from the academic year 2008 – 09)

SPEECH AND LANGUAGE PROCESSING

No. of Credits: 4

UNIT I

Introduction: The role of knowledge in speech and language processing, Language, Thought, and Understanding, Past and present developments, Future directions.

Regular Expressions and Finite Automata: Regular expressions, Finite state automata, Regular languages and FSAs.

Morphology and Finite State Transducers: Survey of English morphology, Finite-state morphological parsing, Combining FST lexicon and rules, Lexicon-free FSTs: The Porter Stemmer, Human morphological processing.

Computational Phonology and Text-to-Speech: Speech sounds and phonetic transcription, The phoneme and phonological rules, Transducers, Mapping text to phones for TTS, Prosody in TTS, Human processing of phonology and morphology.

UNIT II

Probabilistic Models of Pronunciation and Spelling: Introduction to spell checking, Spelling error patterns, Probabilistic models, Bayesian method to spelling correction, and pronunciation, Weighted automata, Pronunciation in humans.

N-grams:  Counting words in corpora, Simple N-grams, Smoothing, Backoff, Deleted interpolation, N-grams for spelling and pronunciation, Entropy.

HMMs and Speech Recognition: Speech recognition architecture, Advanced methods for decoding, A* decoding, Acoustic processing of speech, Computing acoustic probabilities, Training a recognizer, Waveform generation for speech synthesis, Human speech recognition.

UNIT III

Word Classes and Part-of-Speech Tagging: English word classes, Tag-sets for English, Part-of-speech tagging, Rule-based part-of-speech tagging, Stochastic part-of-speech Tagging, Transformation-based tagging.

Context Free Grammars for English: Constituency, Context-free rules and trees, Sentence-level constructions, The noun phrase, Coordination, Agreement, The verb phrase and sub categorization, Spoken language syntax, Grammar equivalence and normal form, Grammars and human processing.

Parsing with Context Free Grammars: Parsing as search, A basic top-down parser, Problems with the top-down parser, The Earley algorithm, Finite-state parsing methods.

UNIT IV

Lexicalized and Probabilistic Parsing: Probabilistic context-free grammars (PCFGs), Problems with PCFGs, Probabilistic lexicalized CFGs, Dependency grammars, Human parsing.

Representing Meaning: Computational requirements for representations, Meaning structure of language, First order predicate calculus, Representing linguistically relevant concepts.

Semantic Analysis:  Syntax-driven semantic analysis, Semantic attachments for a fragment of English, The design of a syntax-driven analyzer, Integrating semantics into the Earley algorithm, Idioms and compositionality, Robust semantic analysis.

UNIT V

Machine Translation: Language similarities and differences, The transfer metaphor, The interlingua, Direct translation, Using statistical techniques, Usability and system development.

Language Processing in Indian Languages: Phonetics for Indian languages, Applicability of Paninian grammar for Indian language processing, Lexical resources, and NLP tools for Indian languages.

Text Book:

Jurafsky D, and Martin H M, Speech and Language Processing, Pearson Education, 2000.

Reference Book:

Bharati A, Chaitanya V, and Sangal R, Natural Language Processing – A Paninian Perspective, PHI, 1995.

MCS 505                                                                                                                         TOP

SRI VENKATESWARA UNIVERSITY :: TIRUPATI

M.Tech (CSE) - (CBCS)

(With effect from the academic year 2008 – 09)

FUZZY LOGIC AND INTELLIGENT INFORMATION SYSTEMS

No. of Credits: 4

UNIT I

Introduction, Basic Concepts of Fuzzy Logic, Fuzzy Sets.

UNIT II

Fuzzy Relations, Fuzzy Graphs, and Fuzzy Arithmetic, Fuzzy If-Then Rules.

UNIT III

Fuzzy Implications and Approximate Reasoning, Fuzzy Logic and Probability Theory.

UNIT IV

Fuzzy Logic and Artificial Intelligence, Fuzzy Logic in Database and Information Systems.

UNIT V

Fuzzy Logic in Pattern Recognition, Fuzzy Model Identification.

Text Book:

Yen J, and Langari R, Fuzzy Logic-Intelligence, Control and Information, Pearson Education, 2004.

MCS 506                                                                                                                         TOP

SRI VENKATESWARA UNIVERSITY :: TIRUPATI

M.Tech (CSE) - (CBCS)

(With effect from the academic year 2008 – 09)

TOPICS IN ADVANCED COMPUTING

No. of Credits: 4

UNIT I

Introduction to pervasive computing, Information access devices, Smart identification.

UNIT II

Embedded controls, Entertainment systems.

UNIT III

Operating systems and Middleware components required for pervasive Computing.

UNIT IV

Introduction to the grid, OGSA and WSRF, Semantic grid and autonomic computing.

UNIT V

Grid monitoring, Grid scheduling and resource management, Workflow management for the grid.

Text Books:

1.            Hansmann U, et al, Pervasive Computing Hand Book, Springer, 2001.

2.            Li M, and Baker M, The Grid: Core Technologies, Wiley, 2005.

Reference Book:

Jankiram D, Grid Computing – A Research Monograph, Tata McGraw-Hill, 2005.