Course Catalog

To provide insight into current research problems in the area of Applications of Computer Science. The exact contents are of computer application may differ every year depending on the course run under this category.

The course emphasis is on special topics and research problems in the emerging areas.

The course emphasis is on special topics and research problems in the emerging areas.

Course description not available.

The goal of the Computer Vision course is to provide hands-on knowledge on applying popular Computer Vision techniques to handle images and videos. The students of this course will be given opportunities to do one research project and a set of assignments. The course curriculum is designed to equip the students with the recent advances in Computer Vision.

This course will introduce students to basic building blocks of cryptography and applications of cryptographic protocols in real world. The focus will be on how cryptography and its application can maintain privacy and security in electronic communications and computer networks.

Course description not available.

This course will introduce students to fundamentals of information security, cryptography, access control mechanisms, system attacks and defenses against them.

Students will learn to develop the large programs such as editor, parser, Lex and Yacc etc.

Project-2/Internship

Course description not available.

Course description not available.

The goal of deep learning course is to provide a hands on knowledge on applying deep learning techniques to handle large data. The students of this course will be given opportunities to do one research project and a set of assignments.

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

This course briefs about Computer Structure, the Algorithmic approach to solve a problem, basic introduction to computers and its corresponding concepts for the benefit of students. Apart from this, programming concepts are also discussed in this course using C programming language.

Principles of Programming Languages

Throughout the course, students will be expected to demonstrate their understanding of Discrete Mathematics by being able to use mathematically correct terminology and notation, construct correct direct and indirect proofs, use division into cases in proof, use counterexamples and apply logical reasoning to solve a variety of problems

This course includes the introductory and advanced concepts and implementation of the Object Oriented Paradigm using any 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, etc.

Introduction to Computer Architecture: Overview and history - The cost factor, Performance metrics and evaluating computer designs, Memory hierarchy. Instruction set design - Assembly / machine language, Von Neumann machine cycle, Microprogramming / firmware, Memory addressing, Classifying instruction set architectures, RISC versus CISC Pipelining - General considerations, Comparison of pipelined and nonpipelined computers, Instruction and arithmetic pipelines, examples, Structural hazards and data dependencies, Branch delay and multicycle instructions, Superscalar computers. Memory System Design - Cache memory, Basic cache structure and design, Fully associative, direct, and set associative mapping, Analyzing cache effectiveness, Replacement policies, Writing to a cache, Multiple caches, Upgrading a cache, Main Memory, Virtual memory, structure, and design, Paging, Replacement strategies, Secondary memory Multiprocessors and Multiple Computers - SISD, SIMD, and MIMD architectures, Centralized and distributed shared memory- architectures, Cache Coherence

Data management has been becoming increasingly critical to derive value to existing applications and services. This course is designed to cover advanced concepts of data management including (but not limited to) concurrency control, transaction management, query processing, indexing, mobile data management, spatial databases, as well as handling WWW & social media data. The course has a significant hands-on lab component, where students will do programming assignments to further improve their expertise in the concepts and implementation of advanced data management systems. Unit 1: Concurrency Control This unit will cover topics such as the need for concurrency control, serializability, recoverability, optimistic & pessimistic concurrency control mechanisms, two-phase locking, two-phase commit, time-stamp ordering, multi-version concurrency control etc. Unit 2: Transaction Management This unit will cover topics such as the ACID properties of transactions & the relaxation of some of these properties for new-age applications, rollback, deadlocks, compensating transactions, recovery. Unit 3: Indexing This unit discusses various important single-dimensional and multi-dimensional database indexes as well as their variants. Examples include B-trees, R-trees, quadtrees etc. In this unit, students will also learn how to create variants of these fundamental indexes to improve query response times for real-world complex user queries related to domains such as smart cities. The unit also covers the inherent trade-offs associated with each of the indexes so that students can learn how to decide the appropriate index to use for a given application scenario. Unit 4: Complex Query Processing & Optimization This unit covers query processing & optimization. Topics in this unit include (but are not limited to) query plans, query size estimation, disk I/O cost estimation etc. This unit will also cover the processing of complex spatial database queries such as multi-way spatial joins, keyword search queries in spatial databases, k-Nearest Neighbor queries, m-closest descriptors queries and so on. Furthermore, this unit will also cover aspects of distributed query processing such as query processing in a cluster environment and issues such as data migration, data replication, index migration & replication, data availability, performance, scalability etc. Unit 5: Mobile Data Management Given the ever-increasing popularity and prevalence of mobile devices and apps, the need for effective mobile data management continues to increase dramatically. This unit describes key mobile data management issues such as mobile resource constraints (e.g., energy, bandwidth), incentives for participatory crowdsourcing/crowdsensing, reliability, scalability etc. Unit 6: Handling WWW & Social Media Data This unit will discuss existing as well as emerging applications of data management for WWW & social media data. Key issues associated with handling WWW & social media will also be covered. Examples of such issues include noisy data & data reliability, data heterogeneity, data integration, data semantics, knowledge management, unstructured data, scalability etc.

This course introduces the concepts and techniques in the field of artificial intelligence. It is aimed for undergraduate students who have knowledge of Data structures and any imperative programming language such as C, C++, Java, etc. AI is a broad area consisting of various courses under its umbrella such as Neural Network, Soft Computing, Machine Learning, Natural Language Processing, Vision, etc., But this course imparts broad overview, both of traditional and modern AI, and prepares a student for advanced elective courses as mentioned above.

Data Mining

The theory of computation teaches how efficiently problems can be solved on a model of computation, using an algorithm. It is also necessary to learn the ways in which the computer can be made to think. Finite state machines can help in natural language processing which is an emerging area.

Data and Knowledge Engineering

The course discusses the system level issues, serializability, concurrency control, transaction management, and recovery. It addresses the issue of a database implementation, query processing and query optimization for relational databases. Different file structures, indexing, and hashing techniques will also be addressed. The course will also introduce the management of Big Data and data warehouse.

Fundamentals of digital image processing, image enhancement using point processing, edge detection, noise removal, line detection, corner detection, morphological operations on binary images, texture determination, video processing and motion estimation, image processing in frequency domain, filtering, image compression, DCT, JPEG, object detection and classification, digit recognition, face recognition using Machine Learning and Deep Learning,

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.

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.

In this course, we would explore the fundamental data mining methodology, OLTP and OLAP, data pre-processing, association rules mining, clustering, classification, and other advanced topics in the field such as Social impact of Data mining, Recent trends in Data mining research, Challenges and Future Scope, need for Security and Privacy preserving in Data Mining.

The course will enable the students to appreciate the power of analytical models in the analysis of the performance of computer communication networks.

Wireless sensor networks (WSNs) have attracted a wide range of disciplines where close interactions with the physical world are essential. The distributed sensing capabilities and the ease of deployment provided by a wireless communication paradigm make WSNs an important component of our daily lives. The course covers the basic concepts of WSN from a system perspective and application development. This course deals with comprehensive knowledge about wireless sensor networks. It provides insight into different layers and their design considerations.

Course description not available.

This course includes the working of Computer Systems, Instruction Level Architecture, Instruction Execution, current state of the art in memory system design, and I/O devices. It also includes the concept of microprogramming, parallel architecture and pipelining techniques.

M.tech Thesis -1

Optical Networks

Research Methodology

Machine Learning

Cyber Physical Systems

Course description not available.