This course provides an opportunity for the students to work as an individual to produce a system. The students will carry out an individual project and submit a dissertation. The students will also make a presentation of the project and face for a viva.
Evaluation Method: Defend the Project Proposal 10%, Interim progress report 15%, Publication 15%, Final report/presentation 60%

Research Seminar introduces the process of academic research and allows the students to open their mind to problem-solving strategies based upon formal inquiry and the detailed research of others. It will also include participating in discussions and giving presentations.

This course will provide an opportunity for students to advance their understanding of research through critical exploration of research language, ethics, and approaches, Introduction, Scientific Research, Reading and recording, Critiquing research papers, Mind mapping and Concept Mapping, Planning and conducting research, The research process, Types of computing research , Computing research methods , Ethics and plagiarism, Research ethics, Minnesota ethics guidelines , Plagiarism , Data collection and analysis, Communicating research findings, Simple Latex for Academic Writing , Citation styles, Citation style guides, Planning and delivering oral presentations , Presentation Tips

Introduction to Artificial Neural Networks, Multi-Layer Perceptrons, Back-Propagation Algorithm, ANNs using Tools, Pre-Processing Techniques, Feature Extraction & Selection algorithms, Naïve Bayes Learning and Bayesian Belief Learning Models, Unsupervised Learning Methods, Learning Vector Quantization Methods, Self-Organizing and Neural Gas Algorithms, Hierarchical Learning Models, Combining Multiple Classifier Models, Measuring Classifier Performance.

Dierent Perspectives of En- terprise Modeling: Goal Modeling, Business Rules Modeling, Process Modeling, Business Modeling. Developing Goal Models, Business Rule Models and Business Models. Business work ow Modeling with Event Driven Process Chains (EPC), Work ow analysis with Petri nets. State of the art developments in Enterprise Modeling, Latest Research Trends, and Modeling Approaches.
Evaluation Method: End Semester Examination

Introduction, Introduction to data mining, Introduction and overview to the Business Intelligence, the need of Business Intelligence, Business intelligence solutions, Data, Quality of data, Supervised learning and unsupervised learning.

Introduction to High Performance Computing, Performance Analysis, Serial performance optimization, Parallel Algorithm design methodologies, Parallel Programming Models, Programming with Distributed Memory, MPI, Programming with Shared Memory, OpenMP, Load balancing and Data Partitioning, Grids and Clouds, GPU computing

Introduction to Bioinformatics, Archives and Information Retrieval, Pairwise sequence Alignments: Dot Plots, Simple Alignments. The Needleman and Wunsch Algorithm, BLAST algorithm, FASTA algorithm, Multiple sequence alignments: Introduction to multiple sequence alignments, Greedy approach, Star alignment approach and ClustalW algorithm, Hidden Markov models, Phylogenetic Trees: Introducing distance measures and distance based phylogenetic tree building algorithms UPGMA, NJ, introducing character-based measures and character based phylogenetic tree building algorithms: maximum parsimony and maximum likelihood. Protein and RNA Structure Prediction: Secondary Structure, Tertiary and Quaternary Structure, Tools for Modeling Protein Folding, Introduction to drug discovery and ligand docking algorithms, micro array data analysis.

Evaluation Method : Continuous Assessment : 30% and End Semester Exami- nation 70%

Compiler Design Introduction,Phases of compiler, Lexical Analysis- Deterministic Finite Automata, Regular expressions, Lex& Flex (Lexical analyzer generator), Context Free Grammar- Derivative, Parse trees, Ambiguous grammar, EBNF, Top-Down Parsing- LL(1) parse table, Recursive decent parser, Bottom-up parsing- LR parsing,Yacc, Abstract syntax trees, Semantic Analyser

The focus of this course is to understand service orientation of technology. The course will provide a deep understanding of Service Oreiented Architecture (SOA) fundamentals from Application as well as Infrastruture perspective. The web Service architectures and standards will be discussed in the course unit. Introduction to Service Oriented computing, Standards Bodies of SOC, Service Oriented Architecture, Traditional vs Service Oriented Architecture, Service Classification, Enterprise service Bus, Web Services, Web Service stack, Introduction to SOAP, WSDL, Introduction to BPEL4WS, Service management and governance, Introduction to Cloud Computing.

Review of Sets, Relations, Functions and Related Matters, Review of Propositional Logic, and Logical Arguments, Introduction to Predicate Calculus, Concepts of programming language and Proof of Correctness, Hoare Logic, Formal Methods, Use of Z-Notation for various aspects of Program constructs and Verification

Introduction to AI Techniques, State and State Space, Heuristic Search Techniques, Resolution and Theorem Proving, Semantic Net and Partitioned Semantic Nets, Conceptual Dependency Diagram and Scripts, Minimax Theorem and Alpha-Beta Pruning, Natural Language Processing

This course provides students with a understanding of different new concepts related to Computer Science.The detailed contents may be changed from year to year depending on the current trends and available expertise.