



Semesterwise Curriculum for M. Tech. in CSE with specialization in Software Engineering (SE) / Data Analytics (DA) 








1st Semester: 





Course No 
Course Description 
Credits 

Mathematical Structures for Engineers 
4 

Advanced Software Engineering 
4 

Data Mining 
4 

Program Elective – I 
4 

Technical Writing and research Methodology 
Pass/Fail 
Total Credits = 16 






2nd Semester: 





Course No 
Course Description 
Credits 

Advanced Data Structures and Algorithms 
4 

For specialization in Software Engineering: Functional and Nonfunctional Testing For specialization in Data Analytics: Machine Learning and Pattern Recognition 
4 

For specialization in Software Engineering: Information Security and Cyber Laws For specialization in Data Analytics: Data Warehousing and Business Intelligence 
4 

Open Elective – I 
4 

Technology, Society and Environment 
Pass/Fail 

M. Tech Thesis Work 
4 
Total Credits = 16 






3rd Semester: 





Course No 
Course Description 
Credits 

M. Tech Thesis Work 
18 
Total Credits = 18 






4th Semester: 





Course No 
Course Description 
Credits 

M. Tech Thesis Work 
18 
Total Credits = 18 









Course Sketch: Mathematical Structures for Engineers 





Category: Core course  First year PG Program (ECE/CCE/CSE) Credits: 4 (300) Prerequisites: Objectives of the Course Students entering PG program usually find that their mathematical foundation is inadequate to pursue research for their thesis. It is also a fact that, for them to achieve the required level of mathematical maturity entirely through selfstudy is difficult. This course is designed with an objective to provide the essential knowledge required to remove this inadequacy. The content of the course is designed keeping in mind the mixed audience coming from electronics and communication engineering and computer science and engineering disciplines. At the conclusion of this course, students should have a sound understanding of what mathematics is about, and should have acquired a level of mathematical literacy that would enable them to see its relevance in their own domain of knowledge. Course Outline
 Sets, Relations and Functions [4]: Order, Equivalence and Correspondence
 Groups, Rings and Fields [3, 5]: Permutations, Symmetries, Polynomials
 Vector Space [5]: Basis, Linear transformations, Norm, InnerProduct, Orthogonality, Metric space introduction [4], Finite Dimensional Vector Space [5]: System of linear equations, Eigen values, Eigen vectors, Matrix inverse, Least squares and Pseudo inverse, Change of basis and similarity transform (25 Hrs)
 Introduction to Graphs and Connections with Linear Algebra: Adjacency and Incident matrices, Graph spectrum, Graph Partitioning and Clustering, Shortest path algorithms [6] (6 Hrs)
 Numerical Techniques: Basic concepts of round off errors, floating point arithmetic, convergence, Numerical schemes for Bisection method, Newton Rapson method, error analysis for iterative methods; computing roots of polynomials, Direct method for solving linear systems, numerical factorization and Eigen value problems [7] (09 Hrs)
Course Outcomes CO1 Students will understand the need of various mathematical structures and also be able to model a given engineering problem mathematically. CO2 Students will be able to grasp the concepts of data (data processing system) represen tation and will be able to use the basic results from linear algebra and function analysis to process them. CO3 Students will read and simulate a well established algorithm from a research paper in the domain of their specialization in groups. CO4 Students will write a term paper on any one of the mathematical structures taught in the course. CO5 Student will analyzed the different techniques for the efficient numerical solution of problems in Science and Engineering References
 V.L. Hansen. Functional Analysis: Entering Hilbert Space. World Scientific, 2006.
 D.G. Luenberger. Optimization by Vector Space Methods. Series in Decision and Control.Wiley, 1969.
 A. Papantonopoulou. Algebra: Pure & Applied. Prentice Hall, 2002.
 G.F. Simmons. Introduction to Topology and Modern Analysis. International series in pure and applied mathematics. Krieger Publishing Company, 2003.
 G. Strang. Introduction to Linear Algebra. WellsleyCambrige Press, 2003.
 Narsingh Deo. Graph Theory. Pretice Hall India, 2003.
 Steven C. Chapra, RP Canale: Numerical methods for engineers. Tata Mc Graw Hill 5 th Edition, 2006.






Course Sketch: Advanced Data Structures and Algorithms 





Course Objectives This course introduces students to the analysis and design of computer algorithms. Upon completion of this course, students will be able to do the following:
 Analyze the asymptotic performance of algorithms.
 Demonstrate a familiarity with major algorithms and data structures.
 Apply important algorithmic design paradigms and methods of analysis.
 Synthesize efficient algorithms in common engineering design situations.
Prerequisites Data Structures & Algorithms, Discrete Mathematics Data structures: Dynamic Data structures; 23 trees, Redblack trees, binary heaps, binomial and Fibonacci heaps, Skip lists, universal hashing. Data structures for maintaining ranges, intervals and disjoint sets with applications. Sorting : Merge Sort, Quick Sort, Heap sort, Lower bound of sorting, Counting Sort, Radix Sort Divide and Conquer: Integer multiplication, Tromino Tiling, Strassen’s Matrix multiplication, Counting inversions, Closet Pair of point in 2 Dimensions, Linear time selection algorithm Greedy Algorithms: Interval scheduling, Huffman Coding, Fractional Knapsack Graph Algorithms: adjacency list and adjacency matrix representations of graphs, Breadth First Search(BFS), Applications of BFS like Shortest Path on unweighted graph ,to check whether a graph is bipartite or not, Depth First Search(DFS),Application Of DFS like Topological Sort, Cycle Detection, Checking Whether a Digraph is Strongly connected or not, Minimum Spanning Tree: Kruskal’s Algorithm and its implementation using Union Find Data structure, Prim’s Algorithm and its implementation using heap data structure Shortest Path: Bellman ford algorithm, Dijkstra’s Algorithm, Application of Shortest path algorithms Dynamic Programming: Weighted interval scheduling, 01 Knapsack Problem, Matrix chain multiplication, Longest Common subsequence NP and Computational Intractability: Polynomialtime reductions, the definition of NP, NPcomplete problems References:
 Algorithms by Dasgupta, Papadimitriou and Vazirani, Pub: Tata McGrawHill.
 The Design and Analysis of Computer Algorithms by Aho, Hopcroft and Ullman, Pub Addison Wesley (Indian reprint available)
 Algorithm Design: by Kleinberg and Tardos, Low Priced Ed. by Pearson.
 Introduction to Algorithms by Cormen, Leiserson and Rivest, Stein, Pub: MIT Press (Indian reprint by Prentice Hall)
 The Algorithm Design Manual by Steve Skiena. Pub: Springer






Course Sketch: Advanced Software Engineering 





Software Architecture, Cloud Computing, Architecting Software for Cloud Environment, Software Quality Assurance (SEICMM), Software Reliability, Knowledge Based Software Engineering, Agent Oriented and Aspect Oriented Software Engineering, Semantics and Context Aware Software Applications References:
 A BRIEF GUIDE TO CLOUD COMPUTING, An essential guide to the next computing revolution, by Christopher Barnatt Publisher: Constable & Robinson
 Software Architecture in Practice (2nd Edition) by Len Bass, Paul Clements and Rick Kazman; SEI Series in Software Engineering
 Advanced Software Engineering: Expanding the Frontiers of Software Technology: IFIP 19th World Computer Congress, First International Workshop on ... in Information and Communication Technology) [Paperback] by Sergio F. Ochoa and GruiaCatalin Roman (Nov 24, 2010)
 Software Engineering: An Advanced Course (Springer Study Edition) by F. L. Bauer (Apr 24, 1986)
 IEEE and ACM Journals in the area of Software Engineering and Practice






Course Sketch: Functional and NonFunctional Software Testing 





Goals and Role of Software Testing, Types of Software Testing, Functional Testing, Unit, Integration and Interface Testing; System level, User Acceptance cum Production Testing; Defect Tracking and Resolution; Regression Testing; NonFunctional Testing – Stress and Volume Testing, Reliability, Availability and Portability Assessment; Test Management and Automation; Test Case and Data Generation; Test Metrics and Software Test Effort Estimation Techniques Reference:
 Software Testing by Patton and Patel, Pearson Education
 Foundations of Software Testing by Aditya P. Mathur, Pearson Education
 Software Testing by M G Limaye, Tata McGrawHill
 Software Engineering – A practitioner’s approach by Roger S. Pressman, 5th Edition, McGraw Hill






Course Sketch: Information Security and Cyber Laws 





Principles of Information Security, Information Security Threats and attacks, Security in Mobile and Wireless Computing, Security Threats to Ecommerce, Model of Cryptographic Systems, Security metrics, Cyber Crime Types & overview of Cyber Crimes and Laws References:
 Merkov, Breithaupt, “Information Security”, Pearson Education.
 Schou, Shoemaker,”Information Assurance for the Enterprise”, Tata McGraw Hill.
 Sood, “Cyber Law Simplified”, Mc Graw Hill.
 Furnell, “Computer Insecurity”, Mc Graw Hill.
 IT Act 2000.






Course Sketch: Data Mining 





 Introduction: Basic Data Mining Tasks, Data Mining Issues, Data Mining Metrics, Data Mining from a Database Perspective.
 Data Mining Techniques: A Statistical Perspective on Data Mining, Similarity Measures, Decision Trees, Neural Networks, Genetic Algorithms.
 Classification: StatisticalBased Algorithms, DistanceBased Algorithms, Decision TreeBased Algorithms, Neural NetworkBased Algorithms, RuleBased Algorithms, Combining Techniques.
 Clustering: Similarity and Distance Measures, Hierarchical Algorithms, Partitional Algorithms, Clustering Large Databases, Clustering with Categorical Attributes.
 Association Rules: Basic Algorithms, Parallel and Distributed Algorithms, Incremental Rules, Advanced Association Rule Techniques, Measuring the Quality of Rules.
 Advanced Techniques: Web Mining, Spatial Mining, Temporal Mining.
References
 J. Han and M. Kamber. Data Mining: Concepts and Techniques, 2nd Ed. Morgan Kaufman. 2006.
 K. Pujari: Data Mining Techniques, Universities Press, 2009
 P. Tan, M. Steinbach, V. Kumar: Introduction to Data Mining, AddisonWesley. 2006
 M. H. Dunham. Data Mining: Introductory and Advanced Topics. Pearson Education. 2001.
 D. Hand, H. Mannila and P. Smyth. Principles of Data Mining. PrenticeHall. 2001.
 H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. 2000.






Course Sketch: Machine Learning and Pattern Recognition 





 Introduction to Machine Learning.
 Introduction to classifier design and supervised learning from data, classification and regression.
 Basics of Bayesian decision theory, Bayes and Nearest neighbour classifiers.
 Theory of Generalization, The VC Dimension, BiasVariance Tradeoff.
 Neural networks for pattern classification and regression.
 Support Vector Machines (SVMs) and some variants.
 Unsupervised learning: clustering and dimensionality reduction.
 Applications of Machine Learning like Anomaly detection, Recommender systems, Optical Character Recognition, Largescale machine learning.
References
 C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
 R.O.Duda, P.E.Hart and D.G.Stork, Pattern Classification, John Wiley, 2002.






Course Sketch: Data Warehousing and Business Intelligence 





 Basic architectural concepts for business intelligence and data warehousing
 Industry terminology
 Data Integration Framework (DIF)
 Hubandspoke, federated, and independent architectures
 Topdown, bottomup, and hybrid data warehousing methodologies
 Dependencies between data warehousing architecture and development methodology
 How to assess the cost and value implications of various architectures
 How to assess the timetodelivery implications of various methodologies
 Project management implications of various approaches
 How to determine the bestfit architecture and methodology for your data warehousing program
 Best practices
 Industry trends
References
 Multidimensional Databases and Data Warehousing, Christian S. Jensen, Torben Bach Pedersen, Christian Thomsen, Morgan & Claypool Publishers, 2010
 Data Warehouse Design: Modern Principles and Methodologies, Golfarelli and Rizzi, McGrawHill, 2009
 Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications, Elzbieta Malinowski, steban Zimányi, Springer, 2008
 The Data Warehouse Lifecycle Toolkit, Kimball et al., Wiley 1998
 The Data Warehouse Toolkit, 2nd Ed., Kimball and Ross, Wiley, 2002







