[nilesh]

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This year I have taken admission in M.Tech CSE Course this year at MATS University. I need syllabus to start course, so do you know what should I do to download syllabus of M.Tech CSE Course offered by MATS University?

[Vishal Kotkar]

As you are searching for syllabus of M.Tech CSE Course offered by MATS University, so here I am providing complete syllabus:

MATS University M.Tech CSE Syllabus

Semester I
CSE101 Algorithms and Algorithmic Complexity
CSE102 Cryptography and Network Security
CSE103 Advanced Database Management Systems
CSE104 Elective – I *
CSE105 (P) Systems Design Lab. - I

Semester II
CSE201 VLSI Design
CSE202 Gr. A – Image processing
Gr. B – Embedded Systems
CSE203 Mobile and Wireless Computing
CSE204 Elective – II *
CSE205 (P) Systems Design Lab. – II

Elective-II : (Any one)
Real-time Systems
Advanced Software Engineering
Cluster and Grid Computing
Natural Language Processing

Semester III
Project - I # 200
Seminar * 100
General Viva-Voce 100

Semester IV
Project - II ## 400

Detailed Syllabus:

CSE101 : Algorithms and Algorithmic Complexity
Fundamentals of Mathematics: Linear Algebra, Combinatorics, Boolean Functions, Number Theory.
Fundamentals of Algorithms: Classification of Problems, Complexity, Asymptotic Notations.
Recurrences: Master Theorem
Probabilistic Analysis: Sort, Search, Random Binary Search trees, Red-black trees, Priority Queues, Bipartite Matching, Common Subsequence Problem, Flow Networks, Ford-Fulkerson Method, Fast Fourier Transforms, Knuth-Morris-Pratt Algorithm, Convex Hull, Point Location.
Combinatorial Algorithms: Generating Permutations, Generating Partitions.
Approximation Algorithms: Concept, Design, Applications. In approximability. Number -Theoretic Algorithms. Randomized Algorithms, Primality Testing, Constrained and Unconstrained Optimization, Evolutionary Algorithms.

CSE102 : Cryptography and Network Security
Principles of Security, Basic Cryptographic techniques, Classification of attacks, Virus, Worm, Trojan Horse, Spam etc.
Symmetric Key Cryptography : Algorithm types and modes, Cryptographic Algorithms
Asymmetric Key Cryptographic Algorithms, Digital Signature
Digital Envelope, Message Authentication Code, Message Digest
Public-Key Infrastructure (PKI)
Authentication: Classifications, Mutual authentication Algorithms, Kerberos
Security in layers and domains: IPsec, Secure Socket Layer (SSL), E-mail Security
Electronic transactions

CSE103 : Advanced Database Management Systems
Distributed Database: Distributed database architecture, levels of distribution transparency, DDB design, Translation of global queries, Query optimization for DDB, Concurrency control for DDB
Object Oriented Database: OO paradigm, OO data models: Object identifiers, Relationship and Integrity, ER Diagramming model for OO relationships, Object relational data models
Data Warehousing: Components, Building a data warehouse, Data extraction, cleanup and transformation, OLAP
Future Trends in data models: Semantic data models, DM for loosely structured data items, Multimedia database.

CSE104(a) : Distributed Systems
Introduction: definition, characteristics and challenges of distributed systems, Architectural models (client-server).
Time: Physical and logical time, Event ordering, Clock Synchronization, Message delivery ordering.
Inter-process communication (sockets, UDP/TCP), Overview of middleware, Web services, RPC.
Operating system support - Mutual exclusion, termination detection, deadlock, process migration, replication management, threads, multi-threaded client/server.
Distributed file service (design options, file sharing, access control).
Distributed transactions (flat/nested, one/two phase commit).
Security - main threats and techniques for ensuring security (secure channels, firewalls).
Fault-tolerance and availability (passive/active replication, gossip architectures).
Applications. Pervasive computing environments: active office, home and city, Events, composite events, mobility and location-tracking, Electronic health, police and transport services.

CSE104(b) : Bioinformatics
Basic Biology: What is life? The unity and the diversity of living things. Prokaryotes and Eukaryotes, Yeast and People, Evolutionary time and relatedness, Living parts: Tissues, cells, compartments and organelles, Central dogma of molecular biology, Concept of DNA, RNA, Protein and metabolic pathway. What is Bioinformatics? Recent challenges in Bioinformatics.
Biological databases: Their needs and challenges. Example of different biological databases – sequence, structure, function, micro-array, pathway, etc.
Sequence Analysis: Theory and Tools: -Pairwise alignment – Different local and global search alignment, Heuristic searches (like BLAST) applicable to search against database, Multiple alignment algorithms, Whole genome comparison.
Walk through the genome: Prediction of regulatory motifs, Operon, Gene, splices site, etc.
Markov models: Hidden Markov models – The evaluation, decoding and estimation problem and the algorithms. Application in sequence analysis.
Molecular phylogeny: maximum Parsimony, distance Matrix and maximum likelihood methods. Concepts of adaptive evolution.
Application of graph theory in Biology: Biochemical Pathway, Protein-protein interaction network, Regulatory network and their analysis.

CSE104(c) : Soft Computing
Soft Computing: Introduction, requirement, different tools and techniques, usefulness and applications.
Fuzzy sets and Fuzzy logic: Introduction, Fuzzy sets versus crisp sets, operations on fuzzy sets, Extension principle, Fuzzy relations and relation equations, Fuzzy numbers, Linguistic variables, Fuzzy logic, Linguistic hedges, Applications, fuzzy controllers, fuzzy pattern recognition, fuzzy image processing, fuzzy database.
Artificial Neural Network: Introduction, basic models, Hebb's learning, Adaline, Perceptron, Multilayer feed forward network, Back propagation, Different issues regarding convergence of Multilayer Perceptron, Competitive learning, Self-Organizing Feature Maps, Adaptive Resonance Theory, Associative Memories, Applications.
Evolutionary and Stochastic techniques: Genetic Algorithm (GA), different operators of GA, analysis of selection operations, Hypothesis of building blocks, Schema theorem and convergence of Genetic Algorithm, Simulated annealing and Stochastic models, Boltzmann Machine, Applications.
Rough Set: Introduction, Imprecise Categories Approximations and Rough Sets, Reduction of Knowledge, Decision Tables, and Applications.
Hybrid Systems: Neural-Network-Based Fuzzy Systems, Fuzzy Logic-Based Neural Networks, Genetic Algorithm for Neural Network Design and Learning, Fuzzy Logic and Genetic Algorithm for Optimization, Applications.