Department of Computer Science and Engineering

Module 1 (10 (T) + 7(P) Hours)

New Computing Paradigms & Services: Cloud computing , Edge computing , Grid computing , Utility computing , Cloud Computing Architectural Framework, Cloud Deployment Models, Virtualization in Cloud Computing, Parallelization in Cloud Computing, Security for Cloud Computing, Cloud Economics , Metering of services.
Module 2 (10 (T) + 7(P) Hours)

Cloud Service Models: Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Service Oriented Architecture (SoA), Elastic Computing, On Demand Computing, Cloud Architecture, Introduction to virtualization.
Module 3 (10 (T) + 7(P) Hours)

Types of Virtualization, Grid technology , Browser as a platform, Web 2.0, Autonomic Systems, Cloud Computing Operating System, Deployment of applications on the cloud, Case studies- Xen, VMware, Eucalyptus, Amazon EC2.

Module 4 (12 (T) + 7(P) Hours)

Introduction to Map Reduce, Information retrieval through Map Reduce, Hadoop File System, GFS, Page Ranking using Map Reduce, Security threats and solutions in clouds, mobile cloud computing, Case studies- Ajax, Hadoop.

References:

1. 
   Tom  White, Hadoop:  The  Definitive  Guide,  O'Reilly  Media,  2009.
   
2. 
   Jason  Venner,  Pro  Hadoop,  Apress,  2009.
   
3. 
   Timothy Chou , Introduction to cloud computing & Business, Active Book Press, 2010.
   
4. 
   Current literature- Journal & conference papers
   

CS4038 DATA MINING

Pre-requisite: Nil

L	T	P	C
3	0	2	4

Total Hours: 70 Hrs  

Module 1 (10 (T) + 7(P) Hours)

Introduction to data mining-challenges and tasks Data preprocessing, data analysis, measures of similarity and dissimilarity, Data visualization –concepts and techniques
Module 2 (10 (T) + 7(P) Hours)

Classification- decision tree-performance evaluation of the classifier, comparison of different classifiers, Rule based classifier, Nearest-neighbor classifiers-Bayesian classifiers-support vector machines, Class imbalance problem

Module 3 (10 (T) + 7(P) Hours)

Association analysis –frequent item generation rule generation, evaluation of association patterns

Module 4 (12 (T) + 7(P) Hours)

Cluster analysis,-types of clusters, K means algorithm, cluster evaluation, application of data mining to web mining and Bioinformatics

References:

1. 
   Pang-Ning Tan,Michael Steinbach and Vipin Kumar, Introduction to Data Mining, Pearson Education 2006.
   
2. 
   Han and Kamber, Data Mining: Concepts and Techniques, 2/e, Morgan Kaufmann, 2005.
   

CS4039 MULTI AGENT SYSTEMS

Pre-requisite: Nil

L	T	P	C
3	0	2	4

Total Hours: 70 Hrs  

Module 1 (10 (T) + 7(P) Hours)

Introduction to agent and multi-agent systems, different types of agents, abstract architecture, distributed problem solving, application areas, Software tools for modeling Multi-Agent Systems
Module 2 (10 (T) + 7(P) Hours)

Agent communication, communication languages KQML and FIPA ACL Communication policies and protocols, Protocol Modeling

Module 3 (10 (T) + 7(P) Hours)

Negotiation in multi-agent- agent environment, game theoretical model , heuristic approach, argumentation based approach

Module 4 (12 (T) + 7(P) Hours)

Distributed decision making –evaluation criteria -Social welfare, Pareto Efficiency, Individual Rationality, Stability, Application of multiagent systems in complex distributed problem solving, Modeling distributed multi-agent systems.

References:

1. 
   M. Wooldrige, An Introduction to multi-agent systems, Wiley, 2009.
   
2. 
   R. Norvig, Artificial Intelligence: A modern approach, Prentice Hall, 2010.
   

CS4040 BIOINFORMATICS

Pre-requisite: Nil

L	T	P	C
3	0	2	4

Total Hours: 70 Hrs  

Module 1 (10 (T) + 7(P) Hours)

Molecular biology primer, gene structure and information content, Bioinformatics tools and databases, genomic information content, Sequence Alignment, Algorithms for global and local alignments, Scoring matrices, Dynamic Programming algorithms.
Module 2 (10 (T) + 7(P) Hours)

Introduction to Bio-programming languages, Restriction Mapping and Motif finding, Gene Prediction, Molecular Phylogenetics, Phylogenetic trees, Algorithms for Phylogenetic Tree construction.

Module 3 (10 (T) + 7(P) Hours)

Combinatorial pattern matching, Repeat finding, Keyword Trees, Suffix Trees, Heuristic similarity search algorithms, Approximate pattern matching.

Module 4 (12 (T) + 7(P) Hours)

Microarrays, Gene expression, Algorithms for Analyzing Gene Expression data, Protein and RNA structure prediction, Algorithms for structure prediction. Emerging trends in bioinformatics algorithms and databases.

References:

1. 
   Neil C Jones and Pavel A Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004.
   
2. 
   David W Mount, Bioinformatics- Sequence and Genome Analysis, 2/e, Cold Spring Harbor Laboratory Press, New York, 2004.
   
3. 
   D. E. Krane and M. L. Raymer, Fundamental Concepts of Bioinformatics, Pearson Education, 2003.
   
4. 
   T. K. Attwood and D. J. Parry-Smith, Introduction to Bioinformatics, Pearson Education, 2003.
   
5. 
   Current Literature.
   

CS4041 NATURAL LANGUAGE PROCESSING
Pre-requisite: Nil

L	T	P	C
3	0	2	4

Total Hours: 70 Hrs  

Module 1 (10(T)+7(P) Hours)

Introduction to Natural Language Processing, Different Levels of language analysis, Representation and understanding, Linguistic background. Grammars and parsing, Top down and Bottom up parsers.

Module 2 (10(T)+7(P) Hours)

Transition Network Grammars, Feature systems and augmented grammars, Morphological analysis and the lexicon, Parsing with features, Augmented Transition Networks.

Module 3 (10(T)+7(P) Hours)

Grammars for natural language, Movement phenomenon in language, Handling questions in context free grammars, Hold mechanisms in ATNs, Gap threading, Human preferences in parsing, Shift reduce parsers, Deterministic parsers, Statistical methods for Ambiguity resolution

Module 4 (12(T)+7(P) Hours)

Semantic Interpretation, word senses and ambiguity, Basic logical form language, Encoding ambiguity in logical from, Thematic roles, Linking syntax and semantics, Information Retrieval, Recent trends in NLP.

References:

1. James Allen, Natural Language Understanding, 2/e, Pearson Education, 2003.

2. T Siddiqui and U S Tiwary, Natural Language Processing and Information Retrieval, Oxford University Press, 2008.

3. D Juraffsky and J H Martin, Speech and Language Processing, Pearson Education, 2000.

CS4042 WEB PROGRAMMING

Pre-requisite: Nil

L	T	P	C
3	0	2	4

Total Hours: 70 Hrs