Course Catalog

Course description not available.

Course Summary This course focuses on design and analysis of algorithms for problems that are geometric in nature or modelled as a geometric problem. These include construction convex hulls, Voronoi diagrams, range queries, Euclidean MST and optimization problems like Linear Programming. Besides the well-studied algorithmic techniques, geometric problems have motivated many novel algorithmic strategies and data structures that the course will attempt to cover. Students are expected to be familiar with basic data structures and algorithm design techniques and high school level coordinate geometry. Since geometric problems require appropriate data representation, there will be some exercises that students will be expected to program and experience the challenges of such implmentations. Course Aims 1. To provide students with a basic understanding of representation of geometric objects and computational framework. 2. To introduce students to the design and analysis of fundamental geometric problems. 3. To develop understanding of relationship between complexity of geometric problems. 4. To develop some basic relationship between combinatorial geometry and analysis of geometric algorithms . 5. To enable students to get a flavour of implementing algorithms that rely on arithmetic and algebraic operations involving finite precision operands. Learning Outcomes On successful completion of the course, students will be able to: a) Evaluate the importance of modelling a given problem in terms of its geometric properties and how to exploit these algorithmically. b) Develop a good understanding of many fundamental geometric data structures like range-search trees and their extensions to higher dimensions. c) Develop some understanding of the computational complexity of basic geometric problems. Curriculum Content Syllabus 1. Geometric Fundamentals: Models of computation, lower bound techniques, geometric primitives, geometric transforms 2. Convex Hulls: . Planar convex hulls, higher dimensional convex hulls, randomized, output-sensitive, and dynamic algorithms, applications of convex hull 3. Geometric Searching: segment, interval, and priority-search trees, point location, persistent data structure, fractional cascading, range searching, nearest-neighbor searching 4. Proximity Problems: closest pair, Voronoi diagram, Delaunay triangulation and their subgraphs, spanners, well separated pair decomposition 5. Arrangements: Arrangements of lines and hyperplanes, sweep-line and incremental algorithms, lower envelopes, levels, and zones, applications of arrangements 6. Randomized Techniques:.Use of random sampling and Randomized incremental construction   Teaching and Learning Strategy a) Lectures will encourage students to develop intuitions behind designing efficient algorithms with interactive discourse. b) Extend techniques learned during lectures for solving assignment problems. c) Students will be expected to use online material available in internet to enhance their classroom learning. d) Discussion over email groups outside of class room to promote collective understanding of challenging issues. Teaching and Learning Strategy Class Hours Out-of-Class Hours Lectures 30 hours Programming Exercises 20 hours (estimated) Assignments 20 hours ASSSESSMENT. Assessment Strategy Formative Assessment: a) Assignments b) Programming Project c) Midterm Exam Summary Assessment a) Final Exam 16. Mapping of Learning Outcomes to Assessment Strategy Assessment Scheme Type of Assessment Description Percentage Assignments 10% Programming Assignments Two project-like assignments to be done in group of 2 10%+ 10% Midterm Exam 25% Final Exam 45% Total 100% Bibliography Books: 1. Computational Geometry by M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf, pub: Springer. 2. Computational Geometry in C, J. O' Rourke, Cambridge University Press. 3. Algorithms in Combinatorial Geometry, H. Edelsbrunner, pub: Springer-Verlag (EATCS Monograph) 4. Computational Geometry - an introduction, Preparata and Shamos, pub: Springer-Verlag. 5. Computational Geometry: an Introduction through randomization, K. Mulmuley, Pub: Prentice Hall. 6. LEDA - a platform for combinatorial and geometric computing, Mehlhorn and Naher, pub: Cambridge. Online Resources: NPTEL Videos of a longer version of this course

Information Retrieval

Prerequisite: CSD101 The goal of the course is to teach fundamental data structures, whichallow one to store  collections of data with fast updates and queries. A detailed schedule will evolve as the semester progresses. Key topics will definitely include: C refresher, abstract data types, fundamental data structures, complexity of algorithms, analysis tools, linked lists, stacks and queues, search trees,maps, hashing, priority queues, graphs.

Principles of Programming Languages

Prerequisite: NA Counting: Permutation & Combination, Derangement, Pigeonhole Principle, Binomial Coefficient, Principle of Inclusion and Exclusion.   Set Theory: Operations on sets, Cartesian product of sets, General proofs of some fundamental identities on sets.   Group Theory: Functions: Definition, Classification of functions, Operations on functions, Algebraic Structures: Definition, Groups, Subgroups and order, Cyclic Groups, Cosets, Lagrange's theorem, Normal Subgroups, Permutation and Symmetric groups, Group Homeomorphisms, Definition and elementary properties of Rings and Fields, Integers Modulo n. Relation Algebra:  Relations and Digraphs, Properties of relations, Equivalence relations and equivalence classes, Operations on relations, Connection between relations and some data structures, Transitive Closure and Warshall’s algorithm. Partial order relations, Partial order sets: Definition, Partial order sets, Combination of partial order sets, Hasse diagram. Lattices: Definition, Properties of lattices – Bounded, Complemented and Complete lattice. Graph Theory: Basic of Graph, Euler paths and circuits, Hamiltonian paths and circuits, isomorphic graphs, Connected Graph, Trees, Labeled trees, Tree searching, Undirected trees, Isomorphic trees, Minimal spanning trees, Prim’s algorithm, Planar Graph,  Matching problems, Coloring graphs, Transport networks. Propositional Logic and Predicate Calculus: Propositions and Logical operations, Conditional statements, First order predicate, well-formed formula of predicate, quantifiers, Inference theory of predicate logic.  Methods of proof, Mathematical induction, Recursively defined functions, Growth of Functions, Recurrence relations.

Prerequisite: CSD101 This course includes the introductory and advanced concepts and  implementation of the Object Oriented Paradigm using the Java programming language. Topic would include Introduction, Elementary Programming, Selections, Loops, Methods, Arrays, Strings, Objects and Classes, Inheritance and Polymorphism, GUI Basics and Components, Graphics, Exceptions, Abstract Classes and Interfaces, Event-Driven Programming, Binary I/O, Recursion, and Java Collections Framework. 11.    Course Aims This course will enable the students to understand the concepts of object oriented programming using java, and apply its structures and techniques in various programming problems.

Prerequisite: CSD205 Applications of inferential statistics in engineering problems; Measures of central tendency, Measures of Dispersion, Time series analysis & Trend Analysis. Karl Pearson and Spearman rank correlation, Regression equations and their application, Partial and Multiple correlation & regression. Sampling theory; Formulation of Hypotheses; Application of Z test, t-test, F-test and Chi-Square test & application to engineering problems. Concept of probability and its uses in business decision-making; Addition and multiplication theorems; Bayes’ Theorem and its applications. Probability Theoretical Distributions: Concept and application of Binomial; Poisson and Normal distributions, Introduction to Stochastic Processes.

Prerequisite: CSD201, CSD205 Asymptotic notations, analysis of iterative and recursive algorithms, randomized algorithms, divide and conquer, greedy method, dynamic programming, graph algorithms, backtracking, NP-Hard and NP-Complete problems.

Prerequisite: CSD204, CSD207* Protocol layers, physical and data link layers, multi-access, IP naming, addressing and forwarding, transport protocols, congestion control, routing protocols, wireless networks, quality of service, network security.

: Introduction- Language Processors, the structure of a compiler, Lexical Analysis- the role of lexical analyzer, input buffering, specification of tokens, recognition of tokens, Syntax Analysis- grammars, top-down parsing, bottom-up parsing, LR parsing, Syntax directed translation- definitions, evaluation, application, schemes, Code Generation intermediate code generation, runtime environments, issues in code generator, introduction to optimization. Module 1: Introduction Language Processors, motivation and application, the structure of a compiler, phases of compiler. Module 2: Lexical Analysis The role of lexical analyzer, input buffering, specification of tokens, recognition of tokens Module 3: Syntax Analysis Grammars, top-down parsing, bottom-up parsing, LR parsing Module 4: Syntax directed translation Definitions, evaluation, application, schemes Module 5: Code Generation Intermediate code generation, runtime environments, issues in code generator, introduction to optimization Laboratory: • Programs for Lexical Analysis, exercises based on finite state automata and regular expressions • Programs for Syntax Analysis, exercises based on grammars and pushdown automata • Exercises related to syntax directed translation • Code generation • Code Optimization

Advanced Database Management Systems

Artificial Intelligence

Image Processing and its Applications

Unit 1: •Introduction. Unit 2 (Mobile Radio Propagation): •Types of Radio Waves. •Propagation Mechanisms. •Free Space Propagation. •Land Propagation. •Path loss and Fading. •Doppler Effect, Delay Spread and Intersymbol Interference. Unit 3 (Cellular Concept): •Cell Area. •Signal Strength and Cell parameters. •Capacity of a cell. •Frequency reuse. •How to form a cluster. •Cochannel Interference •Cell Splitting and Cell Sectoring. Unit 4 (Multiple division Techniques): •Concepts and Models of Multiple Divisions (FDMA, TDMA, etc.) Unit 5 (Channel Allocation): •Static Allocation versus Dynamic Allocation. •Fixed Channel Allocation. •Dynamic Channel Allocation. •Hybrid Channel Allocation. •Allocation in specialized System Structure. Unit 6 (Mobile Communication Systems): •Cellular System Infrastructure. •Registration. •Handoff and Roaming Support. •Multicasting. •Security and Privacy. Unit 7 (Wireless MANs, LANs and PANs): •Wireless Metropolitan Area Networks (4G systems). •Wireless Local Area Networks (IEEE 802.11). •Wireless Personal Area Network (Bluetooth Networks).

Introduction to communication networks. Switching: Circuit switching, Packet switching, Message switching, Cell switching, Permanent virtual circuit, Switched virtual circuit. Transmission Medium: Copper cable, Shielded twisted pair, UTP, Coaxial cable, Optical fiber cable. Telephone communication. Data Communication: OSI layers. Data Link layer: HDLC, Multiple access control- ALHO, Polling, CSMA/CD, Token passing. LAN: Ethernet- 10 to 100BaseT, Gigabit, 40 Gigabit, 10Base2, 10Base5, 10Base F, Token ring, FDDI, Repeater, Bridge, Router, Gateway. WAN: Packet switch network- X.25, Frame relay, ATM. Broadband Access Technology: ISDN, Cable modem, xDSL. Internet Protocol: TCP/IP, UDP, IPv4, IP

Prerequisite: CSD101 Introduction, Elementary Programming, Selections, Loops, Methods, Arrays, Strings, Objects and Classes, Inheritance and Polymorphism, GUI Basics and Components, Graphics, Exceptions, Abstract Classes and Interfaces, Event-Driven Programming, Binary I/O, Recursion, Java Collections Framework.

Information Theory

Network Design and Performance

1 Introduction to data mining, Data Mining vs Knowledge discovery, Data Mining issues, Social Implications. 2 Introduction to data warehousing, Database/OLTP systems, Decision Support Systems, Multi-dimensional data model, Data Warehouse components, architecture and implementation, OLAP 3 Data Preprocessing: Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation 4 Statistical Measures: Point Estimation, Models based on Summarization, Bayes Theorem, Hypothesis testing, Regression and Correlation; Similarity measures. 5 Mining Association Rules in large databases, Mining Multidimensional Association Rules from Relational Databases and Data Warehouses, From Association Mining to Correlation Analysis, Constraint Based Association Mining 6 Classification, Prediction, Clustering and Visualization 7 Introduction to Advanced Topics: Mining complex data, Mining Time-series data, Spatial data mining, Multimedia data mining, Text mining, Mining the WWW. 8 Social impact of Data mining, Recent trends in Data mining research, Challenges and Future Scope.

Systems Programming

Sl. No. Topics 1 Introduction to AI, AI History and Applications 2 Intelligent Agents, Rationality 3 Solving Problems by Searching - Generate and Test, Breadth-first search, Depth-first search, Best-First Search, A*, AO, Evolutionary search algorithms 4 Hill Climbing, Minimax Search, Alpha-Beta Cutoffs, State-of-the-Art Game Problems 5 Propositional Logic, Model Checking, First Order Logic, Unification and Lifting, Forward Chaining, Backward Chaining, Resolution 6 Classical Planning 7 Introduction to Probability Theory, Baye’s Theorem, Introduction to Bayesian Networks 8 Understanding and Learning 9 Natural Language Processing-Syntactic Processing, Semantic Analysis 10 Introduction to Expert systems 11 Advanced Topics

Big Data Management and Analytics has been becoming increasingly important for deriving valuable and actionable insights in in several important and diverse domains such as smart cities, transportation, healthcare and financial services. On the other hand, Cloud computing platforms, such as Hadoop, incorporate the capabilities of processing, managing and analyzing such Big Data in a highly scalable manner. This course is designed to equip students with the fundamentals of Big Data management & analytics (including data mining, machine learning techniques etc.) as well as facilitate them in understanding how Big Data can be efficiently processed in Cloud computing platforms. The course also has a significant “hands-on” lab component, where students will gain exposure to processing and analyzing Big Data on Hadoop. Unit 1: Introduction to Big Data and its applications This unit introduces the concept of Big Data and explains its four dimensions (i.e., volume, velocity, variety & veracity). Then it details several applications of Big Data analytics to motivate the ever-increasing importance of Big Data in today’s world. Applications cover a wide gamut of domains ranging from transportation services to finance to social media. Moreover, it describes how Big Data can represent a high value proposition to businesses as a source of competitive advantage in improving some of their key performance metrics such as market share, profit margins etc. Unit 2: Issues associated with Big Data Management This unit discusses various key issue which arise in the processing of Big Data. Notably, many of these issues also arise while processing data that do not fall under the Big Data category. However, such issues are significantly exacerbated due to the tremendously large volumes and typically high complexity of Big Data. Issues include (but are not limited to) data cleaning, data heterogeneity, data integration, replication, caching, maintenance of data consistency, scalability and so on. The unit also covers the inherent trade-offs associated with each of these issues. Unit 3: Concepts of Cloud computing This unit discusses the key concepts and principles of Cloud Computing. It also incorporates detailed information about Cloud-related terminology. The topics covered in this unit include (but are not limited to) pros and cons of Cloud computing, Cloud architecture, Cloud service models (IaaS, PaaS, SaaS), Cloud applications (Azure, AWS etc.), effective resource allocation and cost efficiencies in Cloud computing, multitenancy and so on. Unit 4: Hadoop and MapReduce This unit covers the key concepts of Hadoop and MapReduce for solving real-world analytics problems associated with Big Data. The topics covered in this unit include (but are not limited to) Hadoop Distributed File system and several key Hadoop-related modules or software packages such as Hive, Pig, HBase, Spark, Flume, Sqoop, Oozie etc. Students will not only understand the concepts of these Hadoop packages, but also engage in some hands-on development work on these modules to gain a deeper level of expertise. Unit 5: Data Models & NoSQL This unit discusses the four key data models that are important for handling Big Data. The models are key-value DB, column-family DB, document DB and graph DB. For each of these data models, the unit will cover some of the important real-world technologies from both a theoretical perspective as well as from a practical hands-on point of view. Examples include HBase, Cassandra, Hypertable, BigTable, Dynamo DB, Mongo DB, Neo4J, Redis etc. This unit will also present the various trade-offs associated with selecting an appropriate data model based on issues such as the requirements of the respective applications, the specific properties of the underlying data, complexity of performing analytics and scalability. Unit 6: Big Data Strategy and Implementation This unit examines the business and strategic perspective of Big Data. Topics covered in this unit include (but are not limited to) a brief overview of some of the fundamental concepts of business strategy & business intelligence, understanding the key requirements of the relevant stakeholder(s), defining a Big Data strategy & creating plans for implementing the strategy, selecting appropriate Big Data tools and technologies based on the requirements of stakeholder(s) and cost-benefit trade-offs, maximizing the benefits obtaining by analyzing Big Data and maintaining a sustainable competitive advantage in the market.

Cyber Physical Systems

Prerequisite: CSD301 Key concepts in software project management, planning and its execution. Working knowledge of software project life cycle, create project plan, write business user requirements, estimate the project size, plan Agile sprints, set-up development environment by applying continuous integration and deployment tools, test software project quality, and apply skills to manage stakeholders.

Project

Special Topics in Computer Science

Information Retrieval

Advanced Operating Systems

M.tech Thesis -1

Optical Networks

Advanced Studies in Computer Sciences: Image Processing

Advanced Computer Architecture

Software Project Lab

Advanced Algorithms

Multimedia Computing

Advanced Programming Techniques and Algorithm Design

Advanced Topics in OOP

Wireless and Mobile System

Special Topics in Sensor Networks

Advanced Computer Networks

Internetworking Concepts

Course description not available.

Advanced Probability and Statistics

Self-Study II