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B.Tech Computer Science and Engineering / Information Technology Outline Syllabi

4 YEAR B.Tech COURSES

B.Tech Common core CSE-IT courses for all disciplines (1st year)

15B11CI111: Software Development Fundamentals – I: (1st Sem, 3-1-0 + 0-0-4)

Developing simple software applications with scripting and visual languages:  Elementary Database – Data tables, Basic SQL; Elementary Web Programming - Tagging vs Programming, Introduction to HTML, scripting languages, database connectivity      [10L]

Algorithmic thinking, Introductory algorithms and flowcharts; Data Representation - signed/unsigned integers, real numbers, characters)   [8 L]

Introduction to C programming – syntax and semantics, data types and variables, expressions and assignments, array and struct, simple I/O, conditional and iterative control structures, functions and parameter passing, basic recursion, e.g., factorial, Fibonacci, Programs for pattern generation,

Programs for Elementary numerical problems– unit conversion, average, sum, min, max of a list of numbers,  common operations with vector, matrix, polynomial, and polygons, approximating the square root of a number, finding the greatest common divisor,

Aspects of numerical computing– precision, accuracy, error. Introduction to Scientific Computation. [24 L]

15B11CI211: Software Development Fundamentals – II:  (2nd Sem, 3-1-0 + 0-0-2)

Advanced C programming: Pointers, handling arrays through pointers, pointer arithmetic, Library function - basic file handling, basic graphics and sound functions; linear and binary search, insertion, selection, and bubble sort.  [10 L]

Object Oriented Programming- Concepts of Object-Orientation in C++, constructs, objects,  classes, methods, constructors, function and operator overloading, inheritance, polymorphism, Introduction to SDLC, Program comprehension. Testing fundamentals and test-case generation.   [16 L]

Implementations and applications of elementary data structures- Stacks, Queues, Deque, linked list, binary trees, sparse matrix, Using STL [16 L]

Common core CSE-IT courses for CSE and IT  (1st and 2nd year)

15B11CI212: Theoretical foundations of Computer Science:   (2nd Sem, 3-1-0)

Discrete Structures:Propositional & Predicate Logic, Proof techniques: Sets, Functions,  Recursion, induction, Counting, combinatorics; Relations, closures of relations, equivalence relations, partial orderings, Hasse diagrams, lattices; Graphs, Euler and Hamiltonian paths, planar graphs, graph coloring problem, Boolean algebra, Binary arithmetic, algebraic structures, properties and applications; [30L]

Introduction to Automata theory: Finite Automata and Regular languages, regular expressions, DFA, NFA, non-regular languages, context free languages, Turing machine and its examples.        [12L]

15B11CI311: Data Structures:  (3rd Sem, 3-1-0 + 0-0-2)

ADT, Time and space complexity, analysis of algorithms, Stack & Queue based applications, Recursion removal, Searching,  Sorting, Simple fractal graphics; Binary tree, k-ary tree, BST, Threaded Tree, AVL Tree, B Tree, B+ Tree, Heap and Priority Queue, Hashing, Set, Multiset, Dictionary,  Maps, Graphs and basic algorithms, e.g., traversal, spanning tree, isomorphism. Data structure evaluation. [30 L]

Advanced Programming issues – Class diagram, Templates, STL, Memory management (garbage collection), Assertion, Defensive programming (e.g. secure coding, exception handling), Code reviews, Program correctness (The role and the use of contracts, including pre- and post-conditions), Unit testing, Event-Driven and Reactive Programming, Debugging techniques.   [12 L]

15B11EC314: Introduction to Digital Systems (3rd Sem, 3-1-0 + 0-0-2)

This will have selected topics in three areas - Digital Electronics, Digital Communication, and Digital Signal Processing.  The detailed syllabus as approved in the BoS of ECE deptt.

15B11CI312: Database Systems and Web:  (3rd Sem, 3-1-0 + 0-0-2)

Historical context of Databases and Web, Relational schema, ER, EER, multimedia data types,  entity and referential integrity, Relational Algebra, SQL,  PL/SQL, Data dependencies and normalisation, Transaction, concurrency control and  recovery,  NoSQL database, Web Architecture, SGML, HTML 5, DHTML, CSS, Java script, PHP, database connectivity;  Database Security issues.

15B11CI313: Computer Organisation and Architecture:  (3rd Sem, 3-1-0 + 0-0-2)

Introduction: levels in architecture, virtual machines, evolution of multi-level machines, performance measures for computer systems; CPU organisation, data path and control, instruction execution, Microinstructions, hardwired and micro-programmed control; ISA, stack/accumulator/register-register/register-memory type of architectures, memory addressing, types of instructions – data movement, arithmetic/logic, control flow, Addressing modes, instruction formats,  MIPS and 8085 architecture; Assembly language programming. Assembler, case study of a popular architecture and assembly level programming; Memory Organisation, hierarchical memory structure, cache memory and performance, I/O organization, programmed/interrupt-driven I/O, DMA; Introduction to pipelining.

15B11CI411: CSIT-6 Algorithms and Problem Solving:  (4th  Sem, 3-1-0 + 0-0-4)

Review of Nonlinear Data Structures (Tree, Heap, Graph).  BFS & DFS.

Analysis of Algorithms Asymptotic Analysis:  Growth of Functions and Recurrences; Notations- Big O, big omega, big theta, little o; Formal and  empirical analysis of sorting algorithms-Radix, merge, quick, heap sort. Time-space trade off of algorithms, P, NP, NP-completeness. Greedy Algorithms: Minimum Spanning Trees, Shortest Path Problem, Dijkstra’s algorithm, Fractional Knapsack. Divide and Conquer Algorithms: Polynomial, Merge and Quick Sort.  Backtracking Algorithms: M-coloring problem, Finding Hamiltonian Cycle.  Dynamic Programming: Binomial Coefficient, Chain Matrix Multiplication, Knapsack, LCS.  Branch and bound, Reduction (transform and conquer). String Matching:  Naïve String Matching, Finite Automata Matcher, Rabin Karp matching algorithm. KMP. Graph Algorithms, Network flows,  Compression, Cryptography.

Problem spaces (states, goals and operators), Factored representation (factoring state into variables), problem solving by search - uninformed search (BFS, DFS, DFS with iterative deepening), Heuristics and informed search (hill-climbing, generic best-first, A*), Two-player games (mini-max search), Decision trees

15B11CI412: Operating Systems and Systems Programming: (4th Sem, 3-1-0+0-0-2)

Operating Systems-Introduction, Historical context of Operating Systems, OS Structure, Architecture, Process Concepts, Threads & Concurrency, Scheduling Concurrency & Synchronization issues, Deadlock, Memory Management, Virtual Memory, File System management, Secondary Storage,  Input output management, Introduction to Distributed OS, Fault and Security Issues, Case studies of OS  [32L]

System Programming: -   Memory Addressing, Interrupts and Exceptions, Kernel Synchronization, System Calls, Signals. Block Device Drivers, Character Device Drivers, Network Drivers.  [10L]

Core CSE courses for CSE (3rd year)

15B11CI511: Computer Networks: (5th  Sem, 3-1-0 + 0-0-2)

 Introduction: Uses of  Computer Networks, Layering, Reference Models and their services; Overview of Physical layer; The Application Layer, Principles of Application-Layer Protocols, The World Wide Web, DNS, E-mail services; Transport-Layer Services and Principles,  Multiplexing and Demultiplexing Applications, UDP and TCP, Connection Establishment, Transport Layer Protocols. Flow Control and buffering, Principles of Congestion Control; The Network Layer, IP: the Internet Protocol, IPv4, Ipv6, Routing in the Internet, Congestion control, QOS issues; The Data Link Layer: Services, Error and Flow control mechanism, Error detection and correcting codes, sliding window protocols, Multiple Access Protocols and LANs, LAN Addresses and ARP, Ethernet; Security and Multimedia aspects of application layer, transport layer, network layer and link layer. Introduction to wireless and mobile networks.   Network Threats and Security.

OR

14B1NCI731: Embedded Systems:  (5th Sem, 3-1-0 + 0-0-2)

Introduction to real time and embedded system, Introduction to Intel/ARM micro-controllers (architecture, addressing, peripherals on chip, instructions, interrupt processing, assembly language programming), memory (memory organization, virtual memory and memory management), bus interfaces, serial interface, power aware architecture, system on chip, compilers for  embedded system development. Introduction to real time digital signal processors and wireless sensors; Introduction to  model based design. Networked embedded systems.

15B11CI513: Software Engineering: (5th Sem, 3-1-0 + 0-0-2)

Program comprehension; Program correctness - The concept of a specification, Defensive programming (e.g. secure coding, exception handling), Code reviews, Testing fundamentals and test-case generation, The role and the use of contracts, including pre- and post-conditions, Unit testing, Simple refactoring; Modern programming environments (Code search, Programming using library components and their APIs), Debugging strategies, Documentation and program style

Software process models, Software project management, Tools and environments, Requirement Engineering, Software Design (principles, design paradigms, structural and behavioural models, design patterns, software architecture, refactoring, use of components), Software construction (coding standards, integration strategies)  Software verification and validation (testing, defect tracking), Software evolution, software reliability. Introduction to formal methods.

OR

15B11CI514: Artificial Intelligence:  (5th Sem, 3-1-0 + 0-0-2)

History and foundations of AI.  Problem solving and Intelligent Agents.Problem solving and Search (Blind, Informed, Constraint Satisfaction, Adversarial Search). Knowledge Representation & Reasoning in deterministic environment  (logic, semantic network, frames). Knowledge Representation & Reasoning in Probabilistic environment (Baysian Network). Decision Making, Ontology. Propositional logic, First order predicate logic. Basic Pattern Recognition and Machine Learning:  Linear Classifier, Evaluation Metrics, Cross Validation, Clustering and Classification algorithms, egression and reinforcement learning, introduction  to ANN. Genetic  Algorithms.

15B11CI611: Theory of Computing and Compiler Design:  (6th Sem, 3-1-0 + 0-0-2)

Review of Automata, its types and regular expressions, Equivalence of NFA, DFA and €-NFA, Conversion of automata and regular expression, Applications of Finite Automata to lexical analysis [14 L]

Push down automata Context Free grammars, top down and bottom up parsing, YACC programming specification [12 L]

Syntax directed translation,  S-attributed and L-attributed grammars,  Intermediate code generation, Chomsky hierarchy of languages and recognizers, Context Sensitive features like type checking, type conversions, equivalence of type expression, Turing Machine as language acceptors and its design, Code generation and optimization. [16L]

OR

15B11CI612: Theory of Programming Languages: (6th  Sem, 3-1-0 + 0-0-2)

Notions of syntax and semantics of programming languages; introduction to operational/natural semantics of functional and imperative languages. Data abstractions and control constructs;

block-structure and scope, principles of abstraction, qualification and correspondence; parameter passing mechanisms; runtime structure and operating environment; practical and implementation issues in run-time systems and environment; abstracts machines; features of functional and imperative languages; the untyped and simply-typed Lambda calculus' type systems for programming languages, Logic Programming,  Concurrent Programming

Core IT courses for IT (3rd year)

15B11CI511:Computer Networks: (5th  Sem, 3-1-0 + 0-0-2) Same as that in CS-1

15B22CI521:Cloud based Enterprise Systems: (5th  Sem, 3-1-0 + 0-0-2)

XML Programming (XML, DTD, XMLschema, XSLT, XQuery), Server Side programming (Java servlet, Java server pages, Database connectivity with Servlet and JSP, MVC Architecture, Struct Architecture and Hibernate Architecture), Component based programming (Java Beans, Enterprise Java beans: (Entity Beans, Session Beans, Message Driven Beans),  Web services (REST,SOAP, UDDI, WSDL, JSON)    [27 L]  

Introduction to Cloud Computing: Public, private, and Hybrid clouds – SPI Model – SPI-X Model-  Architectural Design of Compute and Storage Clouds – Public Cloud Platforms – GAE,  AWS and Azure.  Implementation of Virtualisation: Virtualization structures/tools and mechanism – virtualization of CPU, Memory and I/O devices – Virtual clusters and Resource management – Virtualization for data-center.    Cloud Programming and software environments: Features of cloud platforms- Service Oriented Architecture for distributed computing – Cloud programming paradigms – Map reduce – Hadoop library from apache – programming support of Google App Engines – Programming Amazon AWS and Microsoft Azure – Emerging cloud software environments.   [15 L]  

15B22CI621 : Data Mining and Web algorithms: (6th  Sem, 3-1-0 + 0-0-2)

Introduction data mining, Different Types, Measurement Scales and Similarity Measures of Datasets,  of Data Mining systems, Data Warehouse and OLAP Technology, Multidimensional Data Model, Data Preprocessing, knowledge representation, Attribute-oriented analysis, Classification and Prediction, Accuracy and Error measures, evaluating the accuracy of a Classifier or a Predictor, Ensemble Methods, Clustering, Association Rules, Outlier Analysis, Mining Time-Series Data, GraphMining, Multi-relational Data Mining, Multidimensional Analysis. Text Mining,  Web Mining, Crawling, web Search and retrieval, Evaluating search effectiveness, Web Caching algorithms, Website Optimization Algorithms, Semantic Web, Indexing, Ranking algorithms, Semantic Search, Ontology Mapping, Match Making, Recommendation Algorithms, Clustering/community algorithms, Topical locality.

OR

15B22CI622: Mobile Applications and Internet of Things:  (6th Sem, 3-1-0 + 0-0-2)

Mobile computingArchitecture, Mobile Devices, Mobile System Networks, Data Dissemination, Mobile Management, Security, Handling Mobile Databases, Mobile Application Development Framework and Tools, J2ME, Android Programming – Installation Procedure of various tools that are required for Android Programming, Programming activity, intent, and multiple activity. Interface Development for Mobile Apps, Intents and Services, Storing and Retrieving Data, Mobility and Location Based Services, Communications, Web Telephony, Notifications and Alarms, Graphics, Multimedia.   [27 L]

Introduction to IOT(Architecture, designs, Practices, IOT Hardware (ResberyPi,  Custom Hardware), Architecture of Resbery Pi, Input output ports, programming Tools, Open Hab Framework (Details of Protocols, Details of Libraries of Open hab. Web, Device interfaces and its portability on Android, Windows, web) etc. Exploring Open Hab using java and C/C++ making apps using java and C/C++, testing the apps using emulator and running with resberypi, Introduction to Wearable on Android and Apple, Samsung wears and their programming tools and sample Apps.  [15 L]

Core Courses for students of other Departments after 1st year

EC-xx Data Structures and Algorithms:  (5th Sem, 3-1-0 + 0-0-2)

ADT, Time and space complexity, Data structure evaluation,  Analysis of Algorithms.  Asymptotic Analysis:  Growth of Functions and Recurrences; Notations- Big O, big omega, big theta, little o; Time-space trade off of algorithms, Stack & Queue based applications, Recursion removal, Searching,  Sorting, Binary tree, k-ary tree, BST, Threaded Tree, B Tree, Heap and Priority Queue, Hashing. Greedy Algorithms. Divide and Conquer Algorithms. Backtracking Algorithms. Dynamic Programming.   Branch and bound, Reduction (transform and conquer). Graph Algorithms, e.g., traversal, spanning tree, isomorphism.

ELECTIVES:

10B1NCI737 Multimedia Computing

Introduction to multimedia, multimedia authorizing and processing tools, multimedia data representation,  Basics of image , Color models, Image  representation:   Graphics  Interchange  format,  Tagged  image  file  format,  JPEG, Basics of Digital Audio, Audio   Representation on Computers, Acoustic models and speech signal processing,  Speech Transmission, Differential   pulse  code   modulation, Adaptive  differential  PCM, Basics of Digital Video, Video Signals and formats, Digital Video Processing, Digital Image ,Audio and Video Encoding, Audio Video   Compression and Standards:  MPEG,  MPEG4, MPEG7, H.261, 263, 264, Linear  Predictive  Coding,  Perceptual  Coding,  Audio  Coders , Audio  and  Video  Synchronization, Standards   for multimedia communications.  TCP – based  system  for  multimedia  streaming, Quality of multimedia data transmission, Quality of Service (QoS) for Multimedia over IP, Multimedia protocols RTP, RTCP, RSVP, RTSP. Media on demand (MOD), interactive TV, VoIP protocols,  Peer to Peer  streaming  topologies,     distribution  control  and  privacy   protection   for  Internet   media  delivery, Analysis   of individual   images, text   recognition, and similarity based searches in image databases. Audio   Analysis, Video Analysis,

15B1NCI732   Social Network Analysis

Introduction, Network Concept : Introduction: Graphs, Paths and components, Adjacency Matrices, Ways and Modes, Matrix Product, node degree, types of nodes and types of ties, actor attributes, Random network models : Erdos-Renyi , Barabasi-Albert , Watts-Strogatz small-world model, shortest  path, six degree of separation, Multivarient techniques for Network Analysis : Introduction, Multidimensional scaling, correspondence analysis, hierarchal clustering, Social Network Visualization : Tools, Characterizing whole network : Cohesion, reciprocity, Transitivity and clustering Coefficient, Triad census, Network centrality: Undirected Non-valued networks: Degree, Eigenvector, Beta, k-step reach, betweeness. Directed Non-valued networks: : Degree, Eigenvector, Beta, k-step reach, closeness. Valued Networks Negative tie Networks, subgroup: Cliques and groups, Community Detection: clustering, community structure, modularity, overlapping communities, Information Diffusion: Cascading Behavior: Decision Based Models of Cascades, Cascading Behavior: Probabilistic Models of Information Flow.

15B11CI514 -Artificial Intelligence

Intelligent agents and its environment; Problem solving using search: uninformed and informed searches; Constraint satisfaction problems; Adversarial Search (games, alpha beta pruning, elements of chance); Knowledge representation:: FOPL, Syntax and semantics, Inference in FOPL;  Planning: Partial order planning ; Uncertainty: Probabilistic reasoning, Bayesian rule, Bayesian network ;AI in Learning;  Natural Language Processing.

17B1NCI731 - Machine Learning & Natural Language Processing

Introduction: Introduction to Machine Learning & NLP, Challenges & Requirements, Mathematical Foundation: Probability Theory, Vector Spaces, Matrix algebra, Probability, Data representation, Tokenization, Lemmatization, Parts of Speech Tagging: Various Models: Hidden Markov Model, SVM, CRF, RNN, LSTM, Parsing: Linguistic Essentials, Markov Models, Applications of tagging, Probabilistic parsing - CFG, CSG, PCFG, Document classification: Supervised: Bayesian,  Naive Bayes, N-gram model, sentiment analysis, text classification, Unsupervised: K-means, Expectation-Maximization (EM) algorithm, MaxEnt classifier, Topic Modelling: Latent Dirichlet Allocation (LDA) and its Variants, Applications: Document summarization, Co-referencing,  noun phrase chunking, named entity recognition, co-reference resolution, parsing, information extraction, Machine Translation, Spell Correction, News Article Title Generation, Code Categorization, Question Answering (Eliza)

17B1NCI742 -Algorithms and Artificial Intelligence

Sorting algorithms: O(N2) sorting, Heap, Quick and Merge sorting, Graph Algorithms: DFS, BFS, Shortest path algorithms; Algorithm Design Techniques: Greedy, Divide and Conquer and Dynamic Programming techniques.

Artificial Intelligence approaches: Problem Solving as Search, State Spaces, Graph searching, Heuristic Search. Constraint Satisfaction problems. Reasoning Under Uncertainty: Probability, Independence, Belief Networks, Probabilistic Inference.

Game Playing algorithms: Game trees and alpha beta pruning. Introduction to Natural Language Processing. AI in Learning.

16B1NCI634 Agile Software Development (4 Credits)

Introduction to Agile and Lean Software Development: Basics and Fundamentals, Agile Principles: Agile Manifesto, Scrum and Extreme Programming, Twelve Practices of XP, Agile project management: communication, planning, estimation, quality, risk metrics and measurements, Agile Requirements : User Stories, Backlog Management, Agile Architecture : Feature-Driven Development, Agile Risk Management: Risk and Quality Assurance, Agile Review : Agile Metrics and Measurements, Agile Testing : Test-Driven Development, User Acceptance Test, Scaling Agile for large projects : Scrum of Scrums, Team collaborations, Case studies based on Agile methodology.

17B1NCI736 Bioinformatics Algorithm (4 Credits)

Algorithms and Complexity: Introduction, Biological Algorithms versus Computer Algorithms. Molecular Biology: Introduction, Structure of Genetic Materials, Structural Formation of Proteins, Information Passage between DNA and Proteins, Evaluation of Bioinformatics. Exhaustive Search: Restriction Mapping, Regulatory Motifs in DNA Sequences, Profiles, Search Trees, Finding a Median String. Greedy Algorithms: Genome Rearrangements, Sorting by Reversals, Approximation Algorithms, Breakpoints, Motif Finding. Dynamic Programming Algorithms: DNA Sequence Comparison, The Manhattan Tourist Problem, Edit Distance and Alignments, Global Sequence Alignment, Scoring Alignments, Local Sequence Alignment, Alignment with Gap Penalties, Multiple Alignment, Gene Prediction, Statistical Approaches to Gene Prediction, Similarity-Based Approaches to Gene Prediction, Spliced Alignment. Divide-and-Conquer Algorithms: Divide-and-Conquer Approach to Sorting, Space-Efficient Sequence Alignment, Block Alignment and the Four-Russians Speedup, Constructing Alignments in Sub-quadratic Time. Graph Algorithms: Graphs and Genetics, DNA Sequencing, Shortest Superstring Problem, DNA Arrays as an Alternative Sequencing Technique, Sequencing by Hybridization, SBH as a Hamiltonian Path Problem, SBH as an Eulerian Path Problem, Fragment Assembly in DNA Sequencing, Protein Sequencing and Identification, The Peptide Sequencing Problem, Spectrum Graphs, Protein Identification via Database Search, Spectral Convolution, Spectral Alignment. Combinatorial Pattern Matching: Repeat Finding, Hash Tables, Exact Pattern Matching, Keyword Trees, Suffix Trees, Heuristic Similarity Search Algorithms, Approximate Pattern Matching. Clustering and Trees: Hierarchical Clustering, k-Means Clustering, Evolutionary Trees, Distance-Based Tree Reconstruction, Reconstructing Trees from Additive Matrices, Evolutionary Trees and Hierarchical Clustering, Character-Based Tree Reconstruction. Applications: BLAST: Comparing a Sequence against a Database; The Motif Finding Problem, Gene Expression Analysis, Clustering and Corrupted Cliques, Small and Large Parsimony Problem, Hidden Markov Models, Randomized Algorithms.

16B1NCI731- Combinatorics

Basics of permutations and combinations,  solving linear homogeneous recurrence relations using generating functions, catalon numbers Proof by induction, pigeonhole principle, algebraic structures Proofs based on counting, fermat’s little theorem, set partition, stirling number, derangements, bell numbers, etc Crossing Numbers, Spanning Trees with Low Stabbing Numbers,counting techniques and theorems on graphs. Basic Probability, randomization, randomizedalgorithms Linear programming, primal and dual, algorithms and approximation

10B2NCI731 Computer Graphics (4 Credits)

Introduction:Context, Requirements, and Application; graphics architectures and softwares; Graphics Pipeline and Hardware- Display Unit, Frame buffer, DPU, GPU. Data structures and algorithms for Raster Graphics: Line, circle, ellipse, polygon, Area filling; Rasterization: line drawing via Bresenham's algorithm, clipping, polygonal fill, hidden surface removal. Colours: Color perception, color models (RGB, CMY, HLS), color transformations. 2D and 3D Planer and Curved objects: Data structures for modeling;   Algorithms for Mesh generation, Clipping, 2D and 3D; Geometric  Transformations, and so on; Geometric transformations: affine transformations (translation, rotation, scaling, shear), homogeneous coordinates, concatenation, current transformation and matrix stacks; 3D graphics: classical three dimensional viewing, specifying views, affine transformation in 3D, projective transformations. Rendering and animation: Data Structures, Algorithms and hardware support; Ray Tracing; Shading: illumination and surface modeling, Phong shading model, polygon shading; Discrete Techniques: buffers, reading and writing bitmaps and pixelmaps, texture mapping, compositing; Introduction to animation and keyframing. Procedural modeling: Fractals   and particle systems

17B1NCI732 - Computer and Web Security

Computer Security:-Threats and Vulnerabilities in Computer Security, Authentication and Access Control, Security of Programs and Programming – Unintentional oversights – Buffer overflow problems, malicious code, and countermeasures , OS Security, Rootkits ,  Firewalls – Design, types and configurations. Web Security:- Web Security Model, Web Browser Attacks: Browser Attack Types, Web Attacks Targeting Users, Obtaining User or Website Data, Code within Data, Foiling Data Attacks, Email Attacks Web Application Security:- Cross site scripting, SQL injection attacks, Cross Site Request Forgery Emerging Topics: The Internet of Things: Security in IOT, Security in Fog Computing, Electronic Voting, Cyber Warfare.

16B1NCI733 -Data Compression Algorithms

Introduction: Importance of data compression, Brief history, Compression principles, Compression Performance metrics, Lossless and lossy data compression. Data compression classification, lossless compression: Run length encoding (RLE), Statistical methods-Huffman, Extended Huffman, Adaptive Huffman, Arithmetic Coding Dictionary-based methods, Transforms Lossless image compression, Predictive encoding, JPEG lossless coding, Lossy compression: Distortion measure, Progressive image compression, Karhunen-Loeve Transform (KLT), Singular Value decomposition (SVD), JPEG (Still) Image Compression Standard ,Transform-based coding. Video compression techniques, predictive coding. MPEG video coding, MPEG-1, B-frame predictive coding, MPEG-2, Supporting interlace video. MPEG-2 scalabilities. MPEG video coding -2, MPEG-4, object based video coding, 3D mesh coding. MPEG-4 part 10/ H.264. Audio compressions. Quantization and transmission of audio, pulse code modulation (PCM), Differential coding of audio, lossless predictive coding, DPCM, DM. MPEG audio compression , psychoacoustic, frequency masking, temporal masking, MPEG layers 1-2-3(MP3), MPEG audio strategy, MPEG compression algorithm. MPEG-2 advance coding system (AAC), MPEG-4 audio. Compression performance, Limits on lossless compression, Hardware data compression (HDC).

16B19CI697-Distributed Database Workshop

Introduction to Distributed databases, DDBMS Architecture, Distributed Query Processing, Fragmentation and Replication, SQL vs NOSQL and NewSQL, Distributed databases- Document oriented DBMS (MongoDB), Key-Value stores (Redis), Column Family DBMS (Cassandra), Graph based databases (Neo4j).

17B1NCI748 -  Graph Algorithms and Applications

To develop computational thinking and mapping of problems in form of graph and design of efficient algorithm for the problem. With this course students will be able to Develop the logic for solving graph based puzzles- Develop efficient algorithms for finding a maximum matching and a maximum weight matching in a bipartite graph and able to explore various applications based on bipartite graph matching, Develop efficient algorithms and ability to explore applications based on covering problems, viz. vertex cover, edge cover, and set cover, Explore various applications based on network flow and develop efficient algorithms, Map real life problems on graph, identify the necessary graph concepts to be applied and develop efficient algorithms

17B1NCI746 -Image Processing

Taxonomy of digital images and applications, application domain , RGB, CMYK, HLS, HSV, YIQ, YUV, YCrCb, Half toning, dithering, geometric transformations, enhancement of gray images, color images, and range images, Binary, gray, and color image processing, Fourier and Cosine transformation based processing of gray and color images.Algorithms and domain specific applications ( using boundaries, regions & counters representation), Image segmentation, Image registration, Image features.

16B1NCI436 -Microcontroller Based Systems Design

Introduction to Microcontroller, Programming 8051: Instruction Set, Addressing Modes, Programmers Model, 8051 Hardware: 8051 Hardware Architecture, Basic Block Diagram, Revision of programmers model, SFR, Internal Architecture, Pins & Signals, Delay using S/W, Port Operations. Memory Interfacing, Timer and Counter Timer & Counter configuration and applications, 8051-8255 interfacing with IO Devices: I/O Interfacing, 8255 Configuration, Interfacing with 8051-LED, 7 Segment, Keyboard, and Serial Communication and Interrupts, Serial Interfacing.

12B2NCI752/ 17B1NCI749  -MOBILE COMPUTING                                    

Introduction to mobile computing: Applications, mobile and wireless devices, history of wireless communication, open research topics, simplified reference model, Wireless Transmission, Medium Access Control. Telecommunication Systems: GSM, UMTS, UTRAN, Core Network, Handover. Wireless LAN, IEEE802.11, 802.11b, 802.11a, HIPERLAN, Bluetooth. Mobile network Layer, Mobile transport layer, Mobility, Mobile Operating Systems: Android OS, IOS , Mobile networking, Quality of Service in Mobile Networks, Mobile access to World-Wide-Web, Mobile Data Management, Mobile Transactions, Mobile Computing Models.

16B1NCI833 Nature Inspired Computing

Introduction to search, optimization and decision problems. Classification of optimization problems, notions of local and global search. Introduction to NIA, Classification of algorithms, Significance (Review of NP class problems) and applications. Introduction to Local search techniques: Best first search, Hill Climbing, Simulated Annealing, A star algorithm and Tabu search. Concept of Randomization, Genetic Algorithms: Binary, permutation and integer encoding, crossover and mutation operators, elitism, applications of GA in computer science (0/1 knapsack, TSP etc), Differential evolution, Binary DE and real world applications. Introduction to Swarm Intelligence Based algorithms. Particle Swarm Optimization, Binary PSO and its variants, BAT algorithm , Artificial Bee colony optimization, Flower pollination algorithm, Cuckoo search,  Grey wolf optimizer, Applications to real world problems (e.g. shortest path). Evaluation techniques: Experimentation, No free lunch theorem, existing benchmark functions, test measures, analysis of algorithms. Multi objective optimization: Concept of dominance and Pareto optimality, conflicting and non conflicting objectives, classical methods: Weighted sum, goal programming. Multi objective Evolutionary algorithms: NSGA-II, MOPSO. Multi objective applications (Multi objective knapsack). Handling constraints: Direct methods, Barrier functions, Penalty methods. Hybridization: Hybrid algorithms: Genetic Algorithm + Neural network, combining Local and global search algorithms, combining global search and global search algorithms.

16B1NCI834 -Program Analysis and Transformation

Introduction and optimizing transformations, Program representation- basic blocks & Control flow graph,Data Flow graph, loops and dominators, SSA, Program Analysis – Control flow analysis and Data flow Analysis - dominators, control dependence Analysis bit vectors, iterative frameworks, interval analysis, reaching definitions, liveness analysis , Constant propagation, Common sub expression elimination, dead code elimination, Static-single assignment & optimizations-  Loop optimization, Interprocedural code analysis  and loop transformation, Introduction to LLVM,Applications

16B1NCI642- Wireless Networks

Architecture and applications of current and next generation wireless networks. Key concepts and techniques underlying modern physical layer wireless and mobile communications, Aloha and CSMA based randomized medium access, scheduling for TDMA/FDMA/CDMA-based wireless networks. Network layer routing protocols, key component mechanisms, link metric estimation and neighbourhood table management for proactive and reactive routing protocols, opportunistic routing, backpressure routing, network coding, cooperative routing, routing with mobility and intermittent contacts. Transport layer protocols, with an emphasis on congestion control, including TCP over wireless, congestion sharing mechanisms, explicit and precise rate control, utility optimization-based approaches, and backpressure-based utility optimization. Network simulation software tools, security.

16B1NCI731- Combinatorics

Basics of permutations and combinations,  solving linear homogeneous recurrence relations using generating functions, catalon numbers Proof by induction, pigeonhole principle, algebraic structures Proofs based on counting, fermat’s little theorem, set partition, stirling number, derangements, bell numbers, etc Crossing Numbers, Spanning Trees with Low Stabbing Numbers,counting techniques and theorems on graphs. Basic Probability, randomization, randomizedalgorithms Linear programming, primal and dual, algorithms and approximation

2 YEAR M.Tech COURSES

M.Tech - Computer Science and Engineering - Outline Syllabi

17M11CS112 Machine Learning And Data Mining (3 Credits)

Introduction to Machine Learning, Data Mining and Knowledge Discovery in Data Bases; Introduction to classification, k-Nearest Neighbours, Naïve Bayes, Decision Trees; Linear Regression with One Variable, Linear Regression with Multiple Variables, Logistic Regression; Introduction to Clustering, Different type of Clustering Methods, Partitioning Clustering Methods, Hierarchical Clustering Methods, k-means, k-medoids; Accuracy metrics, Frequent itemset Mining and Association Rules: Frequent itemsets, Apriori algorithm, Association rules; Dimensionality Reduction: Introduction, Subset Selection, PCA, SVD, Factor Analysis, Multidimensional Scaling, Linear Discriminant Analysis; Ensemble Methods: Ensemble methods of clustering for k-means partitions  aggregation, Ensemble methods of classification-Bagging, Boosting, and Random Forest; Artificial Neural Methods: Cost Function, Back propagation, Feed forward Network, Network training, Error Propagation, Application of Neural Networks; Support Vector Machines; Machine Learning Tools: Weka, Scikit Learn;Applications.

15M3NCI231 E-commerce and Social Web

Introduction and overview of E-Commerce, Digital Retail Future, Introduction to Social Commerce, Supporting theories and concepts of Social Commerce, Design principles and features from e-commerce to social commerce, Tools and Platforms of Social Commerce, Social Web marketing, Social shopping,  Customer engagement through Social Web, Social marketing performance metrics, Programming using API and RSS feeds, Implementing Social Commerce applications.

16M2NCI234 Multi-Objective Optimisation (3 Credits)

Introduction of MOOP:Principles of MOOP, Difference with Single Objective Optimization, Linear and Nonlinear MOOP, Convex and Non convex MOOP, Objectives and Non Conflicting objectives, Dominance and Pareto Optimality, Classical Methods of MOOP: Weight Sum Methods, Constraint Methods, Goal Programming Methods, Interactive Methods, Evolutionary Algorithms: Genetic Algorithm, Evolution Strategies, Evolutionary Programming, Non-Elitist Multi-Objective Evolutionary Algorithms: Motivation, VEGA, VOES, WBGA, RWGA, MOGA, NSGA, NPGA, PPES. Elitist Multi-Objective Evolutionary Algorithms: Rudolph Algorithm, NSGA-II, DBPGA, SPEA, TGA, PAES, MOMGA. Constrained of MOEA, Salient Issues of MOEA, and Applications of MOEA.

17MINCI334 Parallel and Distributed Databases (3 credits)

Database architectures: Client-server systems, Parallel vs. distributed databases;  Distributed Database Design Approaches, Fragmentation and allocation; Parallel and Distributed Query Processing; Query Optimization; Parallel DBMS Techniques: Data Placement, Query Parallelism, Parallel query Optimization; Transactions; Publish/Subscribe Systems; Distributed Information Retrieval; Distributed Column Stores and Distributed Graph processing; Distributed Recovery; Stream processing, Mobile databases,  Spatial and temporal databases, Case Studies

13M11CI114/17M12CS127 Distributed Systems

Introduction to Distributed Systems, Characterization of distributed systems, system models, networking and internetworking, inter-process communication, remote invocation, indirect communication, operating system support, distributed objects and components, web services, peer-to-peer systems, security, distributed file systems, name services, coordination and agreement, transactions and concurrency control, distributed transactions, replications, mobile and ubiquitous computing, distributed multimedia systems, Designing distributed systems -  Google Case Study.

17M1NCI131  Flexible Computer Networks

Elements of networking – Ethernet, Wi-fi, 4G/5G , Network Convergence and Unified communications, Flexible networking technologies – Software Defined Network and Network Function Virtualization

Software Defined Networks (SDN) - SDN approach, standards, Data plane and openflow, control plane Open Daylight architecture, Application Plane , Security Issues

Network Function Virtualization (NFV) – Concepts and architecture

Virtual machines, NFV Principles, framework and reference architecture, NFV infrastructure, Virtualized network functions, NFV Use cases

Quality of Service Issues for defining and supporting user needs, Quality of Experience – QoE factors, measurements and applications

14M1NCI331 Mobile and Pervasive Computing (3 Credits)

Software architecture and technologies for mobile and pervasive computing, cellular networks and positioning, mobile computing devices,WAP Architecture, WMLScripts,  RFID technology and applications, location-dependent services, moving objects and location management, data dissemination, context-aware computing, temporal consistency, sensor devices and sensor data management, real-time embedded and surveillance systems, sensor networks,  routing algorithms, data management for pervasive and ubiquitous computing,  User Interface Issues in Pervasive Computing,  Smart Card based Authentication Mechanisms, Wearable computing, Vineyard computing, Naming and Location management in pervasive mobile networks, Replication Services, Synchronization and consistency, Caching, Prefetching and Hoarding, Battery Power management.

14M1NCI334   Web Algorithms 

Web searching algorithms; Web sorting algorithms; Web caching algorithms; Website performance optimization algorithms; Ontology Mapping; Recommendation Algorithms. Google and Facebook patented algorithms

Web evolution; Graph Structure in the Web; Power Law and Lognormal Distributions; Six degree of separation; Social Network Analysis; Link Prediction Algorithm; Community discovery algorithms; Information diffusion Algorithms; Epidemic Models, Web growth models.

M.Tech - Data Analytics - Outline Syllabi

17M11CS112 Machine Learning and Data Mining (3 Credits)

Introduction to Machine Learning, Data Mining and Knowledge Discovery in Data Bases; Introduction to classification, k-Nearest Neighbours, Naïve Bayes, Decision Trees; Linear Regression with One Variable, Linear Regression with Multiple Variables, Logistic Regression; Introduction to Clustering, Different type of Clustering Methods, Partitioning Clustering Methods, Hierarchical Clustering Methods, k-means, k-medoids; Accuracy metrics, Frequent itemset Mining and Association Rules: Frequent itemsets, Apriori algorithm, Association rules; Dimensionality Reduction: Introduction, Subset Selection, PCA, SVD, Factor Analysis, Multidimensional Scaling, Linear Discriminant Analysis; Ensemble Methods: Ensemble methods of clustering for k-means partitions  aggregation, Ensemble methods of classification-Bagging, Boosting, and Random Forest; Artificial Neural Methods: Cost Function, Back propagation, Feed forward Network, Network training, Error Propagation, Application of Neural Networks; Support Vector Machines; Machine Learning Tools: Weka, Scikit Learn;Applications.

17M21CS111 Cloud Based Big Data Systems-1 (3 credits)

Introduction to Database Systems and Cloud Computing, Data Distribution:  Partitioning and Replication, Trade-offs in Cloud Databases, SQL based Cloud Databases, Cloud NoSQL Databases, Cassandra Architecture and Cassandra Data Model, Cassandra Consistency Levels, Cassandra Repair Mechanisms , Transaction Processing , Cassandra CQL Queries

17M25CS113 Data Sciences Programming Lab-I (1 Credit)

Data Analysis Using Excel, Data Science with R- The data science process, Stages of a data science project, Loading Data into R, Workflow: basics, Working with data from files, Working with Relational Databases, Exploring Data, Using summary statistics to spot problems, Spotting problems using graphics and visualization, Managing Data, Cleaning Data,  Data Transformation, Exploratory Data Analysis.

17M27CS111-Project Based Learning-I (Open Data Centric Services Development)

Data collection and handling, Introduction to Datasets, different formats, Data hierarchy, Open Government Data Initiatives, Principles and goals, Various data.gov datasets, Gov Data Platforms for Data scrapping, Python libraries, JSON, RESTful APIs, HTTP APIs, Data parsers, Mashups, Data Visualization Frontends; HTML5, processingJS, jQuery, Visualization with APIs, Graphical algorithms

17MINCI334 Parallel and Distributed Databases (3 credits)

Database architectures: Client-server systems, Parallel vs. distributed databases;  Distributed Database Design Approaches, Fragmentation and allocation; Parallel and Distributed Query Processing; Query Optimization; Parallel DBMS Techniques: Data Placement, Query Parallelism, Parallel query Optimization; Transactions; Publish/Subscribe Systems; Distributed Information Retrieval; Distributed Column Stores and Distributed Graph processing; Distributed Recovery; Stream processing, Mobile databases,  Spatial and temporal databases, Case Studies

16M2NCI234 Multi-Objective Optimisation (3 Credits)

Introduction of MOOP:Principles of MOOP, Difference with Single Objective Optimization, Linear and Nonlinear MOOP, Convex and Non convex MOOP, Objectives and Non Conflicting objectives, Dominance and Pareto Optimality, Classical Methods of MOOP: Weight Sum Methods, Constraint Methods, Goal Programming Methods, Interactive Methods, Evolutionary Algorithms: Genetic Algorithm, Evolution Strategies, Evolutionary Programming, Non-Elitist Multi-Objective Evolutionary Algorithms: Motivation, VEGA, VOES, WBGA, RWGA, MOGA, NSGA, NPGA, PPES. Elitist Multi-Objective Evolutionary Algorithms: Rudolph Algorithm, NSGA-II, DBPGA, SPEA, TGA, PAES, MOMGA. Constrained of MOEA, Salient Issues of MOEA, and Applications of MOEA.