• The literature review: Importance of the literature review, Planning a literature search, Locating relevant literature, Reliability of a source, Note making, Writing a survey and identifying the problem. Planning and writing a research proposal: Research projects, Finding a research problem, and Analysis of research ideas. Planning a proposal: Some general considerations, Proposal outline, Presentation and evaluation of proposals, Students' outline of research work, Major funding agencies.
• Collection and analysis of data: Designing experiments for the purpose of testing research hypotheses, and evaluating the results of those experiments, Analysis of data, Descriptive statistics, Inferential Statistics, Some common statistical tests, Monitoring research, Records to be maintained by researchers, hypothesis testing and inferences. Tables and illustrations: Making of a table, Arrangement of data in tables, Illustrations, Graphs and visualization.
• Information sources: Tools for identifying literature, Types of publications, Indexing and abstracting services, online library, Search engines, Citation indexes, Citations analysis, online searching methods, initiatives for knowledge management. References: How to cite and list correctly, Why citation of source materials is important, Citation and listing system of documents, Common documentation styles, Citation of sources in the text, Examples of writing references, Electronic and on line Sources, Some worked out examples for the prominent styles, Reference management softwares, Selecting a journal, conference.
• Scientific writing: Use of paragraphs and sentence checking. Abbreviations: Care with numerals, SI unit rules and style conventions. Titles, Verbs, Tenses, The active or passive voice,'articles', Prepositions, Relative pronouns, Transitional words and phrases, Economy in words, Singular and plural words, Commonly confused words, Choosing correct words and phrases, Punctuation. Preparation of thesis and research papers: Structure of a thesis/research article/ review article, including title, introduction, literature review, methods and materials, referencing. Editing and proofreading, use of abbreviations.

• C. R. Kothari, Research Methodology: Methods and Techniques, New Age International, 2004, ISBN 8122415229, 978812241522
• Kumar R. Research Methodology: A Step by Step Guide for Beginners (2010) 3rd ed., Pearson Education. (ISBN-13: 978-1849203012)
• Relevant study material from ACM, IEEE, Elsevier, Springer

• Data Structures: Review of basic data structures: stacks, queues, lists (sequential vs linked implementation), trees, and graphs. File-based implementation of B-tree.
• Algorithms: Review of Analysis of Searching, Sorting, Graph Algorithms. Recursion via Greedy Approach and Dynamic Programming. String Matching Algorithms: KMP, Tries Network Flows: Ford and Fulkerson Algorithm. Introduction to Hardness: NP-Complete Class via Clustering and Resource Allocation, Linear Program Vs Integer Programs Introduction to techniques of solving hard problems via Clustering and Resource Allocation: Simple Approximations and Heuristics


• Programming Module Syllabus: Type Conversion and casting: automatic conversion and automatic promotion in a language, promotion rules. Implementation of dynamic data structures, File handling, and exception handling (handling built-in and user defined exceptions),user input through command line arguments, string handling, generic classes and generic functions.

• T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, McGraw-Hill.
• Sara Baase, Computer Algorithms: Introduction to Design and Analysis, Addison Wesley, 1999.
• Kleinberg and Tardos, Algorithm Design.

• Introduction: Optimization problems, neighborhoods, local and global optima, convex sets and functions, simplex method, degeneracy; duality.
• Graph Algorithms: Primal-Dual algorithm and its application to shortest path, Max-flow problems (Ford and Fulkerson labeling algorithms, Dijkstra's algorithm, Ford-Warshall algorithms), bipartite matching algorithm, spanning tree algorithms.

• The course will be taught by Research Papers.

• NP-Hardness and Approximation Algorithms.
• TSP
• Linear Programming and Duality (Vertex Cover and Matching).
• LP Rounding (Set Cover/Vertex cover)
• Dual Fitting (Greedy Set Cover)
• Local Search Technique (Facility Location)
• Primal-Dual Approximation Algorithms in Graph Connectivity and Network Design
• Iterative Rounding

• The course will be taught by Research Papers.

• (NP-Completeness and Approximation)
• Introduction to NP-Completeness and Approximation.
• Problems from first principle: Satisfiability SAT, 3SAT.
• Graphs: Clique, Covering, Graph Partitioning, Subgraph problem, Graph Isomorphism, Graph Coloring, Hamiltonian Cycle Problem, TSP.
• Network Design Problems: Steiner tree, Spanning Trees, Cuts and Connectivity, Routing and Flow Problems.
• Sets and Partitions: Set partition and Covering, Subset sum.
• NP-Hard problems: Clustering Problems like k-means clustering, co-clustering, connected k-means clustering. More new problems as they are added to the class of NPC or NPH.
• Approximation Algorithms for the above problems.

• M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness (Series of Books in the Mathematical Sciences), 1979.
• Teofilo F. Gonzalez, Handbook of NP-Completeness: Theory and Applications 2009.
• Vijay V. Vazirani, Springer-Verlag, Approximation Algorithms, France, 2006.
• Teofilo F. Gonzalez, Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/CRC Computer and Information Science Series) 2007.
• Mokhtar S. Bazaraa, John J. Jarvis, and Hanif D. Sherali, Linear Programming and Network Flows by 2004.
• Part of the course will be covered by research papers.

• Introduction to Graphs, Paths, Cycles and Trails.
• Max-Flow Min-Cut Theorem, Computing Max S-T flows and Min-Cut
• Edge and Vertex Connectivity and Menger's Theorem
• Maximum matching, Hall's Theorem, algorithms for computing maximum matching in weighted and unweighted graphs.
• Edge and Vertex coloring of graphs. Independent sets.
• Planar graphs and algorithms for checking for planarity.

• The course will be taught by Research Papers.

The course needs to focus on survey of work on interactive systems, explore the current and future research areas in interaction techniques and the design, prototyping, and evaluation of user interfaces. Topics include user interface toolkits; design methods; evaluation methods; ubiquitous and context-aware computing; tangible interfaces;
Research papers on these topics need to be discussed with the background that the student is registered in Ph.D. and needs to understand the way papers present the work, conduct the experimental studies and prepare summary on these followed by presentations.
Goals of HCI, Basic Interaction Design Cognitive Psychology of HCI, Graphic design and Human Interaction: Design for social Inclusion, Human Computer Interaction methods, Usability Evaluation.

• The course will be taught by Research Papers.

• Principals of Steganography and Watermarking.
• Information Hiding-Data hiding in images-LSB encoding, BPCS Steganography, Lossless data hiding, data hiding by quantization, Patchwork, Transform domain method, Robust data hiding in JPEG image, DCT based watermarking, Robust welvet based watermarking, Data hiding in binary image, The Zhao-Koch method, The Wu lee method, The CPT method, The TP method, Data hiding in Fax image, Spread Spectrum model.
• Steganalysis - Steganalysis introduction and terminology, Detecting hiding information, Extracting hiding information, Disabling hidden information.

• The course will be taught by Research Papers.

• Symmetric Key Cryptography-Classical Encryption Techniques, Block Ciphers, Data Encryption Standard(DES), Triple DES, Modes of DES, Advanced Encryption Standard, Stream Cipher and RC4.
• Public key cryptography–DH key exchanged, RSA, Elliptic Curve Cryptography, Message Authentication and Hash Function, Cryptographic hash function, Non-Cryptographic Hash function, Birthday problem, birthday attack, tiger hash, HMAC, CMAC.
• Digital Signatures-Cryptanalysis-Enigma, RC4 as used in WEP, Linear and Differential Cryptanalysis attacks on RSA.
• Security Protocols-SSH, SSL, IPSec, Kerberos, WEP, GSM
• Firewalls and Intrusion Detection/Prevention Systems, Wireless Network Security:Firewalls, IDS and IPS, Signature-based and anomaly-based detection, Honeypots and honeynets.
• Post-Quantum Security : Quantum Basics of Quantum Computing, Single and multi-qubit quantum gates, No cloning theorem. Quantum circuits. Quantum Algorithms: Deutsch-Jozsa, Simons, Bernstein-Vazirani, Grover's, Shor's, Quantum attack resistant Digital Signatures.
• Blockchain: Types of blockchain (public, private and semi-private blockchain), application of blockchain. Blocks, Block Headers, Merkel Trees.

• Wade Trappe, Lawrence C. W., Introduction to Cryptography with Coding Theory, Pearson Publication.
• Behrouz Forouzan, Cryptography and network security. 3rd edition (2015), McGraw Hill Education.
• Stallings, W. (2021). Cryptography and Network Security: Principles and Practice (8th Edition). Pearson.
• Research Papers.

• Intelligent Agents: Environments, Intelligent Agents, Agents and Objects, Agents and Expert Systems, Agents as Intentional Systems, Abstract Architectures for Intelligent Agents, Purely Reactive Agents, Perception, Agents with State, How to Tell an Agent What to Do, Utility Functions.
• Practical Reasoning Agents: Practical Reasoning, Means-Ends Reasoning, The Blocks World, Implementing a Practical Reasoning Agent, Commitment to Ends and Means, The Procedural Reasoning System.
• Reactive and Hybrid Agents: Brooks and the Subsumption Architecture. The Limitations of Reactive Agents, Hybrid Agents.
• Multi-agent Interactions: Utilities and Preferences, Multi-agent Encounters, Dominant Strategies and Nash Equilibria, Competitive and Zero-Sum Interactions, The Prisoner's Dilemma.
• Reaching Agreements: Mechanism Design, Auctions, Negotiation, Task-Oriented Domains, Worth-Oriented Domains, Argumentation.
• Communication: Speech Acts, Agent Communication Languages, KIF, KQML, The FIPA Agent Communication Languages, Ontologies for Agent Communication, Coordination Languages.
• Working Together: Cooperative Distributed Problem Solving, Coherence and Coordination, Task Sharing and Result Sharing, Task Sharing in the Contract Net, Result Sharing, Handling Inconsistency, Coordination, Multi-agent Planning and Synchronization.

• Michael Wooldridge, An Introduction to Multi-agent Systems, Wiley Series in Agent Technology, 2002.
• G. Weiss. Multi-agent Systems--A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge, MA, 3rd edition, 2001.
• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (2nd ed.), Pearson Education, 2006.
• Rafael H. Bordini, Jomi Fred Hübner, Michael Wooldridge, Programming Multi-Agent Systems in AgentSpeak using Jason, Wiley Series in Agent Technology, 2007.
• Lin Padgham, Michael Winikoff, Developing Intelligent Agent Systems: A Practical Guide, Wiley Series in Agent Technology, 2004.
• Fabio Luigi Bellifemine, Giovanni Caire, Dominic Greenwood, Developing Multi-Agent Systems with JADE, Wiley Series in Agent Technology, 2007.

• (Rough Granular Computing)
• Rough Sets in Approximation: Parameterized approximation space, uncertainty function, rough inclusion function, lower and upper approximations, properties of approximations such as accuracy and quality of approximation, learning approximation space from data discretization and approximation spaces, Distances and approximation spaces; concept approximation.
• Data Reduction: Reducts in the context if information systems and decision tables, significance of attributes and stability of reducts, representatives in information systems and decision tables.
• Classification and Clustering Methods: Information granulation, decision rules in rough set models, evaluation of decision rules, nearest neighbor algorithms; self-organizing system for information granulation, rough clustering and its evaluation.

• J. Stepaniuk, Rough-Granular Computing in Knowledge Discovery and Data Mining, Springer, 2008.
• P. Doherty, W. Lukaszewicz, A. Skowron, A. Szalas: Knowledge Engineering: A Rough Sets Approach, Springer, 2006.
• Duntsch, G. Gediga, Rough Set Data Analysis: A Road to Non-invasive Knowledge Discovery, Methodos Publishers, Bangor, 2000.
• T. Y. Lin, N. Cercone,
, Kluwer Academic Publishers, Boston, 1997.
• L. Polkowski, A. Skowron (eds.), Rough Sets in Knowledge Discovery 1: Methodology and Applications, Studies in Fuzziness and Soft Computing, vol. 18, Physica Verlag, Heidelberg, 1998.
• S. K. Pal, P. Mitra, Pattern Recognition Algorithms for Data Mining –Scalability

• Real-time and non -real-time applications: Quality of Service (QOS) requirements of real-time applications – bandwidth, delay and delay variation parameters, Quality of service metrics, guaranteed and best -effort services.
• IEEE Wireless LAN (WLAN) standard: 802.11 and 802.11e standards, WLAN services-association, disassociation, re-association, distribution, integration, authentication, de authentication and data delivery services.
• WLAN centralized protocol functions: Point Coordination Functions (PCF), Hybrid
• Coordination Function (HCF), HCF Controlled Channel Access (HCCA); HCCA admission control mechanisms, HCCA parameterized QOS.
• WLAN distributed protocol functions: Distributed Coordination Functions (DCF), Enhanced Distributed Channel Access (EDCA), EDCA priority based QOS.
• Performance analysis of WLAN distributed protocol functions: Random variables and random process, Markov chain model of DCF and EDCA protocols, Throughput and delay analysis.

• Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications IEEE Std. 802.11™-2007.
• G., Bianchi, IEEE 802.11-saturation throughput analysis, IEEE Communication. Letters,vol. 2, pp. 318–320, Dec. 1998.
• G., Bianchi, Performance analysis of the IEEE 802.11 distributed coordination function, IEEE J. Selected Areas in Communication, vol. 18, 535-547, Mar. 2000.
• S., Choi, J.D., Prado, S., Shankar, and S., Mangold, IEEE 802.11e contention-Based channel access (EDCF) performance evaluation, in Proc. IEEE ICC'03, vol. 2, May 2003, pp. 1151–1156.
• S., Mangold, S., Choi, G.R., Hiertz, O., Klein, and B., Walke, Analysis of IEEE 802.11e for QoS support in wireless LANs, IEEE Wireless Communications , December 2003, pp.40-50.
• Y., Xiao, A Simple and Effective Priority Scheme for IEEE 802.11, IEEE Communications Letters, vol. 7, no. 2, February 2003.
• X., Chen, H., Zhai, X., Tian, and Y,Fang, Supporting QoS in IEEE 802.11e Wireless LANS, IEEE Transactions on Wireless Communications, Vol.5, No. 8, August 2006 pp-2217-2227.

• Classification Techniques: Models and Patterns, Performance Measures, Forms of Knowledge, Decision Trees, Linear Regression, Neural Networks, k-Nearest Neighbors, Naïve Bayesian Classifiers, Support Vectors Machines, Ensemble Methods.
• Clustering Techniques: Clustering Concepts, Clustering Vs Classification, Clustering Techniques, Partitioning Methods, Comparing k-Means and k-Medoids, Expectation-Maximization, Hierarchical Methods, Density-Based Methods, Grid-Based Methods, Dealing with Large Data.
• Use of the techniques for web usage mining, user tracking and profiling, web content and structure mining, web personalization, text mining, spatial mining, bioinformatics and other scientific applications.

• D.J.Hand, Heikki Mannila, Padhraic Smyth, Principles of Data Mining, MIT Press 2001, 22
• Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, 2001.
• 3. Soumen Chakrabarti, Mining the Web, Morgan Kaufmann, 2002.
• 4. Sushmita Mitra, Tinku Acharya, Data Mining: Multimedia, Soft Computing, and Bioinformatics, Wiley, 2003, ISBN: 978-0-471-46054-1.
• 5. Ronen Feldman, James Sanger, The Text Mining Handbook, Cambridge University Press, 2006.
• 6. John Wang, Encyclopedia of Data Mining, IGI Global, 2008.

• Introduction to Web Data Management and XML.
• A Survey of Web Data Management Systems: Web Query Systems; Web Information Integration Systems; Web Data Restructuring.
• XML Basics: Semi-structured Data, XML Schemas, XML indexing, XSLT, XHTML, DOM and SAX parsers.
• XML Query Languages: Xquery, Xpath, XsLT, XSQL.
• Node and Link Objects: Representing Metadata of Web Documents and Hyperlinks, Metadata Associated with HTML and XML Documents, Representing
• Structure and Content of Web Documents, Representing Structure and Content of Hyperlinks, Node and Link Objects, Node and Link Structure Trees.
• Databases Modeling: Recent Approaches in Modeling Web Data, Storage of XML data in databases, publishing data from databases in XML. Use of Tools for storing and retrieving data from XML Databases.

• Sourav Bhowmick, Sanjay Madria, and Wee Keong Ng, Web Data Management A Warehouse Approach, Springer.
• Kevin William, Professional XML Databases, Wrox publications.

• Information Hiding: Introduction, Background, and Applications of Information hiding: Data hiding, applications of data hiding.
• Steganography: Frameworks of secret communication, Security of steganography systems, Information hiding in noisy data, Adaptive & non-adaptive algorithms, Active and malicious attackers, Information hiding in written text, Invisible communication.
• Data hiding in still images: LSB encoding, BPCS steganogarphy, Lossless data hiding, Data hiding by quantication, Patchwork, Transform domain methods, Robust data hiding in JPEG images, frequency domain watermarking Detecting malicious tempering, Robust wavelet based watermarking, Kundur-Hatzinakos watermarking, Data hiding in binary images, Zhao-kochmethod, Wu-Lee method, CPT method, TP method, Data hiding in fax images.
• Watermarking: Introduction, Watermarking principals, Applications, Requirements and algorithmic design issues, Evaluation and standards of watermarking.Fingerprinting: Introduction, Terminology and requirements, Classifications, Research history, fingerprinting schemes, Statistical fingerprinting, and Collusion-secure fingerprinting.

• I.J.Cox, M.L.Miller, J.A.Bloom, J.Fridrich, T.Kalker, Digital Watermarking and Stegonagraphy, Morgan Kaufman 2008.
• F.Y.Shih, Digital Watermarking and Stegonagraphy Fundamentals and Techniques, CRC press 2008.
• Stefon Katzeubeisser, F.A.Petitolos, Information Hiding Techniques for Stegonagraphy and Digital Watermarking, Aatech House London 2008.

• Rough Sets: Information Systems, decision tables, indiscernibly relation, set approximation, approximation of family of sets, analysis of decision tables.
• Type-2 Fuzzy Sets: Notion of uncertainty of membership in a fuzzy set, foot print of uncertainty, embedded fuzzy sets, operations on type-2 fuzzy sets, type-2 fuzzy relations, type reduction, type-2 fuzzy inference system.
• Fuzzy Clustering: Limitations of hard partitioning and need for fuzzy clustering, FCM, PCM,GK, and FMLE algorithms, cluster validity measures
• Projected Clustering: The problem of high dimensionality in clustering, use of projected clustering methods to address the problem of high dimensionality–grid based, density based, centroid based, and hierarchical approaches.
• Rough Set Based Methods: Information granulation using rough sets, decision rules in rough set models, classification, and clustering methods based on rough sets.
• Neuro Fuzzy Systems: Neuro fuzzy systems of Mamdani, logical, and Takagi-Sugeno type, flexible neuro fuzzy systems.

• L. Rutkowski, Computational Intelligence, Methods and Techniques, Springer, 2008.
• J. Valente de Oliveira, W. Pedrycz, Advances in Fuzzy Clustering and its Applications, John Wiley & Sons, 2007.
• J. Stepaniuk, Rough-Granular Computing in Knowledge Discovery and Data Mining, Springer, 2008.
• F. Hopner, F. Hoppner, F. Klawonn, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, John Wiley & Sons, 1999.

• Introduction to Swarm Intelligence
• Particle Swarm Optimization
• Ant Colony Optimization
• Job Scheduling on Computational Grid (PSO Application)
• PSO for Data Mining
• ACO for Data Mining
• Solving the Traveling Salesman Problem using Ant Colonies
• Swarm-based Network Management
• Multi-Objective Optimization
• Swarm-Bots

• Eric Bonabeau, Marco Dorigo and Guy Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999
• Proceedings of various recent conferences can be referred to

• The course will be taught by Research Papers.

• Introduction to Theory of NP-Completeness.
• Cook's Theorem, CSP
• Clique Problem.
• Vertex Cover and Independent Set
• Sequencing Problems: Hamiltonian cycle Problem and TSP
• Subset-Set Problem
• Graph Coloring
• Scheduling Problems

• The course will be taught by Research Papers.

• Concept of Software quality, product and process quality, Software Metrics, Software Maintenance Metrics, quality control
• Statistical methods in quality assurance, fundamentals of statistical process control, process capability, Six-sigma quality.
• Bug Reporting, Identification of Bug,Analysis of Bug, Defect management for quality and improvement, Defect fixing Process and fix time prediction modelling
• Basic concepts of reliability, reliability measurements, Software reliability growth modelling,debugging time lags and Fault dependency Modeling,Mathematical Modeling for Multi Release software products.

• N.S. Godbole, Software Quality Assurance: Principles and Practice for the New Paradigm (2nd Ed.), Narosa Publishing, 2017.
• G. Gordon Schulmeyer (4th eds.), Handbook of Software Quality Assurance Artech House,Inc, 2008
• G. O'Regan, A Practical Approach to Software Quality, Springer Verlag, 2002
• Daniel Galin, Quality Assurance: From theory to implementation, Pearson Education Ltd., 2004
• S.H. Kan, Metrics and Models in Software Quality Engineering (2nd ed.), Pearson Education Inc., 2003
• Hoang Pham, Software Reliability, Springer, 2000

• Empirical Software Engineering, Overview of Empirical Studies , Measurements in Software Engineering , Basic Elements of Empirical Research, Systematic Literature Reviews , System Evolution and Evolutionary Metrics ,Quantitative Data Analysis , Qualtitative Data Analysis ,Formulation of Research Questions, Research Variables and Variable selections, Hypothesis Formulation , Detailed Description of Validity Threats, Analysis and Interpretation, Parametric and non parametric test
• Mining Software Repositories: Importance of Mining Software Repositories, Configuration Management Systems,Types of Software Reposotories, Version Contol Systems, Bug Tracking Systems
• Model Development , Interpretation and Permormance Measures, Bug Attributes, Bug Assignment models, Bug Severity Assessment, Bug priority assessment, Identification of duplicate bugs

• Reidar Conradi and Alf Inge Wang " Empirical Methods and Studies in Software Engineering, Springer, Lecture Notes in Computer Science book series (LNCS, volume 2765),2003
• Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A., "Experimentation in Software Engineering" Springer-Verlag Berlin Heidelberg, 2012
• Ruchika Malhotra "Empirical Research in Software Engineering:Concept , Analysis and Applications, CRC Press, 2016

• Introduction: Hypothesis and task, review of gradient descent; logistic regression; cost function- maximum likelihood based cost, cross entropy, MSE; perceptron learning; activation functions – softmax, logistic sigmoid, tanh, ReLU; types of neural networks – feed forward neural network, recurrent neural network, symmetrically connected network.
• Deep Feed Forward Neural Networks: Gradient based learning; hidden units; architecture design; back-propagation; hyperparameters.
• Regularization and Practical Aspects of Deep Learning: Regularization and under-constrained problems, dataset augmentation, noise robustness, early stopping, bagging, dropout, normalizing inputs; vanishing/exploding gradients, weight initialization for deep networks; numerical approximation of gradients; gradient checking; hyperparameter tuning; batch normalization.
• Optimization Algorithms: mini-batch gradient descent; gradient descent with momentum; rmsprop, ADAM optimization algorithm.
• Convolution and Recurrent Neural Networks: Convolution operation; pooling; variants of basic convolution function; efficient convolution algorithms; recurrent neural networks. Deep Models for Text and Sequences: Train a text embedding model, Train an LSTM model. Deep Learning Tools: Use of deep learning tools such as tensor flow for deep learning applications.

• Introduction: Neuron as basic unit of Neurobiology, McCulloch-Pitts model, limitations of single-layered neural networks.
• Supervised Learning: Single-layered neural networks, perceptron rules, Gradient-descent algorithms; Multi-layered neural networks, back propagation algorithm, Radial Basis Function(RBF) networks Unsupervised Learning: Competitive learning, competition through lateral inhibition, Self-Organizing Maps (SOM)
• Kernel methods and support vector machines: binary classification, multiclass classification, allowing for training errors: soft margin techniques
• Convolution Neural Networks (CNNs): Introduction to convolution neural networks, stacking, striding and pooling, applications like image, and text classification.
• Recurrent Neural Networks (RNNs): Introduction to RNNs, bidirectional RNNs and deep RNNs.

• Introduction To Machine Learning: Concept of Machine Learning, Applications of Machine Learning, Key elements of Machine Learning, Supervised vs. Unsupervised Learning, Statistical Learning: Bayesian Method, The Naive Bayes Classifier, Decision Trees.
• Linear Regression: Prediction using Linear Regression, Gradient Descent, Linear Regression with one variable, Linear Regression with multiple variables, Polynomial Regression, Feature Scaling/Selection.
• Logistic Regression: Classification using Logistic Regression, Logistic Regression vs. Linear Regression, Logistic Regression with one variable and with multiple variables, Optimization, Decision Trees vs Logistic Regression
• Neural Networks: Introduction, Model Representation, Gradient Descent vs. Perceptron Training, Stochastic Gradient Descent, Multilayer Perceptrons, Multiclass Representation, Back-propagation Algorithm and related Issues.
• Support Vector Machines:Knowing SVMs, SVM as Large Margin Classifier, Kernels, Using SVMs in Learning complex non-liner functions, Constrained Optimization, SVM utilization.
• Introduction to Clustering, K-Means Algorithm, Random Initialization, Choosing the number of Clusters, Mixture Models, Dimensionality Reduction and its application, Principal Component Analysis (PCA).
• Error Analysis and Anomaly Detection: Utility of Error Analysis, Precision/Recall, Error Metrics for Skewed Classes, Introduction of Anomaly Detection, Gaussian Distribution, Anomaly Detection vs. Supervised Learning.

• Single Processor Architecture and Basic OpenMP Constructs and Functions: Introduction to Parallel Programming, Parallel Architectures and Programming Models, Pipelining, Superpipelining and VLIW, Memory Latency, Cache and Temporal Locality, Cache, Memory bandwidth and Spatial Locality, Intuition for Shared and Distributed Memory Architectures, Shared and Distributed Memory Architectures, Interconnection Networks in Distributed Memory Architectures, Program with a single thread, Program Memory with Multiple threads and Multitasking, Context Switching, Basic thread functions, Shared Memory Consistency Models and the Sequential Consistency Model, Race Conditions, Scoping variables and some race conditions, Thread private variables and more constructs
• Basic Linear Algebra using OpenMP and OpenMP tasks: Computing sum: first attempt at parallelization, Manual distribution of work and critical sections, Distributing for loops and reduction, Vector-Vector operations (Dot product), Matrix-Vector operations (Matrix-Vector Multiply), Matrix-Matrix operations (Matrix-Matrix Multiply), Introduction to tasks, Task queues and task execution, Accessing variables in tasks, Completion of tasks and scoping variables in tasks, Recursive task spawning and pitfalls
• Critical Sections, locks and Matrix Factorization using OpenMP: Understanding LU Factorization, Parallel LU Factorization, Locks, Advanced Task handling, Matrix Multiplication using tasks, The OpenMP Shared Memory Consistency Model
• Distributed Memory programming and Message Passing Interface (MPI): Applications (Finite Element Method, Deep Learning), MPI basic communication routines (Introduction to MPI and basic calls, MPI calls to send and receive data, MPI call for broadcasting data, MPI Nonblocking calls, Application using non-blocking calls: distributed histogram updation), MPI Collectives (MPI Collectives and MPI broadcast, MPI Gathering and scattering collectives, MPI reduction and Alltoall collectives, MPI collectives design)
• Interconnection architectures and performance analysis: Types of interconnects (Characterization of interconnects, Linear arrays, 2D mesh and torus, d-dimensional torus, Hypercube, Trees and cliques), Performance analysis of MPI routines (Hockney model, Broadcast & Reduce with recursive doubling, Scatter & Gather with recursive doubling, Reduce scatter & All gather with recursive doubling, Discussion of message sizes in analysis, Revisiting Reduce scatter on 2D mesh, Reduce scatter & Allreduce on the Hypercube, Alltoall on the Hypercube), Lower bounds and matching algorithms (Lower bounds, Pipeline based algorithm for Allreduce, An improved algorithm for Alltoall on the Hypercube using E-cube routing, Pipeline based algorithm for Broadcast)
• Applications to Graph Algorithms: Breadth First Search (BFS), Minimum spanning tree, Single Source Shortest Path (SSSP) - Dijkstra's and Johnson's algorithms, Transitive Closure and All Pair Shortest Path, Connected Components and Depth First Search, Maximal Independent Sets
• Energy Efficiency and Security Challenges in all types of Wireless Networks

• Introduction to Wireless Networks: History of Wireless Networks - DARPA, ALOHA, PRNET etc. Infrastructure based and Infrastructure less Wireless Networks. Homogeneous & Heterogeneous Wireless Networks. Enabling Technologies & their comparison - ZigBee, Bluetooth & IEEE 802.11, Networking Technologies. Classification of Wireless Ad Hoc Networks - WMNs, MANETs, VANETs & WSNs, Common Challenges, Device Heterogeneity, Introduction to Opportunistic Mobile Networks and UAV networks.
• Wireless Ad Hoc Networks: Media Access Protocols, Routing Protocols - Periodic & On-Demand Routing, Fault Tolerant Routing, Multicast Routing - Flooding, Source Based Multicast Tree(SBT), Core Based Multicast Tree(CBT), Multicast Mesh, Group Based Multicast Forwarding. TCP over Ad Hoc Networks.
• Mobile Adhoc Networks (MANET): Self-organized v/s Conventional Networking. Basic self organizing principles. Design Paradigms in self-organizing Networks, Self-organization Mechanisms - Neural Networks Based, Game Theory Based, Reinforcement Learning Based, Distributed Artificial Learning Based, Data Mining Based, Evolution Theory Based. Co-operation in Mobile Adhoc Networks: Characteristics & Challenges.
• MAC Protocols in MANETs: Classification of MAC Protocols - Carrier Sense Multiple Access (CSMA) Protocol, Multiple Access with Collision Avoidance (MACA), Multiple Access with Collision Avoidance for Wireless (MACAW).
• Routing in MANETs: Introduction, Challenges, Expected Properties. Proactive Routing Protocol - Wireless Routing Protocol (WRP), Fisheye State Routing Protocol(FSR), Optimized Link State Routing Protocol (OLSR). Reactive Routing Protocols - Dynamic Destination Sequenced Distance Vector (DSDV), Dynamic State Routing(DSR), Ad Hoc On-Demand Distance Vector Routing (AODV), Hybrid Routing Protocols - Adaptive Distance Vector (ADV), Unicast Routing Protocols, Multicast Routing Protocols, Tree based Routing Protocols, Mesh based Routing Protocols, Mobility models, Transport layer, Transport Layer Protocols
• Wireless Sensor Networks : Introduction, Coverage and Placement, Topology management, Mobile Wireless Sensor Networks, Medium Access Control, Congestion control, Flow Control, Routing in Wireless Sensor Networks, Underwater WSN, Security.
• Energy Efficiency and Security Challenges in all types of Wireless Networks

• Introduction: Introduction and basic taxonomy of recommender systems (RSs). Traditional and non-personalized RSs. Recommender system functions, Applications of recommendation systems, Issues with recommender systems, Understanding ratings, Overview of data mining methods for recommender systems (similarity measures, clustering, SVMs, dimensionality reduction).
• Collaborative Filtering RSs: User-based nearest neighbor recommendation, Item based nearest neighbor recommendation, Components of neighborhood methods (rating normalization, similarity weight computation, neighborhood selection), Model based and pre-processing based approaches, The cold-start problem, Long-tail principle. Attacks on collaborative recommender systems, Domain-specific challenges in recommender systems.
• Content-based recommendation: Basic components of content-based RSs. Feature selection. Item representation Methods for learning user profiles, High level architecture of content-based systems, Advantages and drawbacks of content based filtering, Item profiles, Discovering features of documents, Obtaining item features from tags, Representing item profiles, Methods for learning user profiles, Similarity based retrieval, Classification algorithms.
• Context-aware recommender systems: Contextual information models for RSs. Incorporating context in RSs.
• Knowledge based recommendations: Knowledge representation and reasoning, Constraint based recommender systems, Case based recommender systems.
• Hybrid approaches: Opportunities for hybridization, Monolithic hybridization design- Feature combination, Feature augmentation, Parallelized hybridization design- Weighted, Switching, Mixed, Pipelined hybridization design- Cascade Meta-level, Limitations of hybridization strategies.
• Performance Evaluation of Recommender Systems: Evaluation metrics, Decision-Support metrics, User-Centered metrics. Experimental settings, Rating prediction and accuracy. Working with RSs data sets.
• Recommender Systems Applications: Real-time Recommender System like Social, Food, Music, Fashion along with their challenges (Any one).

• Introduction: Introduction to Natural Language Processing (NLP) and its applications, ambiguity in language, basics of text processing, language understanding, Part-of-Speech concepts, and formal grammar, spelling correction- edit distance, noisy channel model for spelling correction.
• Language Modelling: Regular expressions, corpus preprocessing, word error handling, language models- unigrams, bigrams, N-grams, smoothing and entropy.
• Computational morphology: Parts-of-speech (POS) tagging, POS tagging using HMM, POS tagging using neural model, model evaluation, lexical semantics, word sense disambiguation, word sense detection.
• Parsing: Introduction to dependency grammars and parsing, top down and bottom-up parsing, treebank; syntactic parsing, transition-based parsing, MST-based dependency parsing.
• Semantics: Vector semantics, words and vectors, TF-IDF, measuring similarity, learning word embeddings, semantics with dense vectors, SVD and latent semantic analysis, skip-gram and CBOW, word sense disambiguation.
• Applications: Text classification, extracting information from text, analyzing sentence structure, building feature-based grammars, analyzing meaning of sentences.
• Sentiment Analysis: Introduction, affective lexicons, learning affective lexicons, computing with affective lexicons, aspect-based sentiment analysis.
• Deep learning models for NLP: Introduction to neural networks, RNN, LSTM and application of these models to NLP tasks.