Course Catalog | Department of Computer Science and Engineering

Course Catalog

CSD488
Special Module in Theoretical Computer Science
1.00
Undergraduate
To provide insight into current research problems in the area of theoretical of Computer Science. The exact contents are of theoretical computer science may differ every year depending on the course run under this category.
CSD487
Special Module in Systems
1.00
Undergraduate
To provide insight into current research problems in the area of systems. The exact contents are of theoretical computer science may differ every year depending on the course run under this category.
CSD486
Special Module in Applications
1.00
Undergraduate
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.
CSD485
Special Module in Artificial Intelligence
1.00
Undergraduate
The students will learn about topics, which are currently at the forefront of Artificial Intelligence research. Each semester, the theme of the course may change depending on the instructor.
CSD484
Special Topics in Theoretical in Computer Science
3.00
Undergraduate
The course emphasis is on special topics and research problems in the emerging areas.
CSD483
Special Topics in Systems
3.00
Undergraduate
The course emphasis is on special topics and research problems in the emerging areas.
CSD482
Special Topics in Applications
3.00
Undergraduate
The course emphasis is on special topics and research problems in the emerging areas.
CSD481
Special Topics in Artificial Intelligence
3.00
Undergraduate
The course emphasizes on special topics and research problems in the emerging areas.
CSD462
Virtualization and Cloud Computing
3.00
Undergraduate
Course description not available.
CSD458
Introduction to Geometric Algorithms
3.00
Undergraduate
Course description not available.
CSD454
Computer Vision
3.00
Undergraduate
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.
CSD452
Big data Analytics
3.00
Undergraduate
Course description not available.
CSD451
Applied Cryptography
3.00
Undergraduate
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.
CSD450
Algorithms for Big Data
3.00
Undergraduate
Course description not available.
CSD363
Social and Information Networks
3.00
Undergraduate
Course description not available.
CSD361
Introduction to Machine Learning
3.00
Undergraduate
The course introduces the basic concepts, techniques and tools for designing programs that learn from data.
CSD356
Foundation of Information Security
3.00
Undergraduate
This course will introduce students to fundamentals of information security, cryptography, access control mechanisms, system attacks and defenses against them.
CSD346
Seminar
2.00
Undergraduate
Students will learn to read, understand and present latest research papers in their chosen area of interest.
CSD345
Software Design Lab
2.00
Undergraduate
Students will learn to develop the large programs such as editor, parser, Lex and Yacc etc.
CSD317
Introduction to Database Systems
4.00
Undergraduate
This course is designed to equip students with knowledge about the fundamentals of Database Management Systems. The course also has a significant lab component. Through this lab component, students will gain exposure to SQL as well as procedural SQL. It includes an introduction to DBMS (Database Management Systems), ER model, relational data model, relational algebra, normalization, indexing, query processing & optimization, transaction processing, concurrency control & recovery, and an introduction to some advanced topics such as data mining, data warehousing, and Big Data.
CSD494
Project-2/Internship
6.00
Undergraduate
Project-2/Internship
CSD360
Introduction to Logic and Functional Programming
3.00
Undergraduate
The course introduces declarative/applicative style of computing. is about describing what to achieve without instructing how to do it. In this category there are mainly two computing paradigms. One is based on resolution and the other on reduction. Logic programming is based on resolution and Functional programming is based on reduction.This course discusses mathematical foundations of these paradigms along with logic language called Prolog and functional language SML.
CSD330
Security Analytics
3.00
Undergraduate
Course description not available.
CSD352
Computational Neuroscience
3.00
Undergraduate
This course introduces basic computational methods for understanding how nervous systems function. Computational principles underlying various aspects of vision, sensory-motor control, learning and memory are studied. Topics such as goal directed behaviour, sleep and consciousness would be discussed. The course has implications in advancement of artificial intelligence, machine learning and robotics as these fields are inspired by how human brain works.
CSD391
Computer Security
4.00
Undergraduate
Course description not available.
CSD355
Foundations of Data Science
3.00
Undergraduate
Course description not available
CSD456
Deep Learning
3.00
Undergraduate
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.
CSD762
Topics in Mathematical Sciences
4.00
Undergraduate
Course description not available.
CSD314
Machine Learning through R.
4.00
Undergraduate
Course description not available.
CSD316
Intro. to Machine Learning
3.00
Undergraduate
Course Summary The course introduces the basic concepts, techniques and tools for designing programs that learn from data. Course Aims a) Understand different types of data. b) Learn how to construct models that can predict from data (supervised learning) and organize data into coherent categories (unsurpervised learning). c) Understand where and how machine learning can go wrong. Learning Outcomes On successful completion of the course, students will be able to: Build models for prediction and data organization from data. Learn to use basic ML libraries. Understand the basic theories and concepts that underly machine learning. Curriculum Content Topics: The learning problem. Types of learning. Training, validation, testing, generalization, overfitting. Features and feature engineering, dimensionality reduction. Bayesian decision theory. Parametric methods. Tree models. Linear models. SVMs and kernel based models. Nearest neighbour models. Markov models. Neural network models. Ensemble methods - boosting, bagging, voting schemes. Distance metrics and cluster based models. The topics in the course will not be covered in linear order. They will be inter-twined to make machine learning easy to understand and hopefully the progression will be fairly logical. Teaching and Learning Strategy Lectures, demonstrations, targeted assignments on conceptual material, term project for integrating the various parts of the course. ASSSESSMENT. Assessment Strategy Midsem (20%), Endsem (35%), programming assignments (15%), term project (30%). Mapping of Learning Outcomes to Assessment Strategy (For each learning outcome listed in Item 12, describe the formative and summative assessment strategy) The midsem and endsem exams will test grasp of theoretical concepts. The assignments will test use of libraries and tools to build and test models. The term project will test the ability to build an end to end system starting from possibly noisy data to construct a high performance model. References a) Ethem Alpaydin, Introduction to Machine Learning, 3rd Ed., MIT Press, 2014. b) Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, CUP, 2012. c) Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012. d) S Kulkarni, G Harman, An Elementary Introduction to Statistical Learning Theory, Wiley, 2011.
CSD411
Intro. to Geometric Algorithms
3.00
Undergraduate
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
CSD358
Information Retrieval
3.00
Undergraduate
This is an undergraduate-level introductory course for information retrieval. It will cover algorithms, design, and implementation of modern information retrieval systems. Topic includes retrieval system design and implementation, text analysis techniques, retrieval models (e.g., Boolean, vector space, probabilistic, and learning-based methods), search evaluation, retrieval feedback, search log mining, and applications in web information management.
CSD101
Introduction to Computing and Programming
4.00
Undergraduate
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.
CSD102
Data Structures
4.00
Undergraduate
This course introduces problem-solving techniques using programs and the design of algorithms and their complexity. It includes an overview of elementary data structures and advanced data structures. Topics would include Time and Space Complexities, Searching, Sorting, Hashing, Basic and Advance concepts in Trees, Priority Queues and Graphs.
CSD203
Principles of Prog. Languages
4.00
Undergraduate
Principles of Programming Languages
CSD204
Operating Systems
4.00
Undergraduate
The topics covered are introductory concepts on processes, threads, process synchronization, CPU scheduling, memory management, storage, file-system, and I/O systems. The topics covered are generic and not tied to any particular operating system.
CSD205
Discrete Mathematics
4.00
Undergraduate
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
CSD210
Introduction to Probability and Statistics
4.00
Undergraduate
Uncertainty is ubiquitous and probability theory provides a rational description. These are several situations in computer engineering and other disciplines, where one tries to cope with probability and uncertainty.
CSD213
Object Oriented Programming
4.00
Undergraduate
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.
CSD304
Computer Networks
4.00
Undergraduate
This course develops an understanding of modern network architectures from a design and performance perspective. It introduces the student to the major concepts involved in wide-area networks (WANs), local area networks (LANs) and Wireless LANs (WLANs) and provides an opportunity to learn the practical aspects using network programming as well.
CSD305
Computer Org & Architecture
4.00
Undergraduate
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
CSD306
Compiler Design
4.00
Undergraduate
: 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
CSD307
Advanced Data Mgmt Systems
3.00
Undergraduate
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.
CSD309
Network Security
3.00
Undergraduate
Course description not available.
CSD311
Artificial Intelligence
4.00
Undergraduate
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.
CSD319
Design and Analysis of Algorithms
4.00
Undergraduate
This course includes the introductory and advanced concepts and implementation of the concepts of asymptotic notations, theoretical and empirical analysis of iterative and recursive algorithms, randomized algorithms, divide and conquer, greedy method, dynamic programming, graph algorithms, backtracking, NP-Hard, and NP-Complete problems.
CSD324
Data Mining
3.00
Undergraduate
Data Mining
CSD326
Software Engineering
4.00
Undergraduate
Software engineering is the branch of computer science that creates practical, cost-effective solutions to computing and information processing problems, preferentially by applying scientific knowledge, developing software systems in the service of mankind. This course covers the fundamentals of software engineering, including understanding system requirements, finding appropriate engineering compromises, effective methods of design, coding, and testing, team software development, and the application of engineering tools. The course will combine a strong technical focus with a capstone project providing the opportunity to practice engineering knowledge, skills, and practices in a realistic development.
CSD334
Theory of Computation
3.00
Undergraduate
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.
CSD338
Information Theory
3.00
Undergraduate
Information Theory
CSD343
Data and Knowledge Engineering
3.00
Undergraduate
Data and Knowledge Engineering
CSD350
Natural Language Processing
3.00
Undergraduate
Natural Language Processing (NLP) is a core area for advancement of Artificial Intelligence Systems and Humanoids, so that these systems can converse like humans. The course introduces fundamental concepts and techniques of natural language processing. Students will gain an in-depth understanding of the computational properties and algorithms for processing linguistic information. NLP has various industry applications like semantic search engines, conversational engines (AI chatbots, virtual agents and humanoids), document summarization systems, knowledge generation systems, speech recognition, text generation and language translators. Students will get an exposure to NLP requirements of Artificial Intelligence industry.
CSD351
Advanced Database Mgmt Systems
3.00
Undergraduate
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.
CSD353
Computer Graphics
3.00
Undergraduate
1. Introduction, CG system, Recursive Fractals, Geometric Objects, Affine Transformations - Translation, Rotation, Scaling, Homogeneous Coordinates, Concatenation. 2. OpenGL Transformations, Projection, Parallel, Perspective, extended Homogenous, Viewing Volumes, Frame Transformations, Clipping, View-Port transformation, Stereo Viewing, Artistic Projection, Non linear projection, Introduction to OpenGL and GLUT. 3. Modeling curves and surfaces, Parametric polynomial curves, Bezier curves, Hermite curves, Splines, B-spline subdivisions schemes, Tensor product surfaces, Surface of revolution, Polygonal meshes. 3D formats: obj and md2, Texture coordinates, Half edge data structures, Back/front faces, hidden line removal using depth buffer. 4. Rendering faces: Gouraud and Phong shading, Ray tracing, Ray casting, Recursive ray-tracing, Ray mesh intersection, Bounding objects, Scene description, Anti-Aliasing, Distributed ray tracing.
CSD357
Image Processing & its Apps.
3.00
Undergraduate
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,
CSD402
Internet and Web Systems
4.00
Undergraduate
Prerequisite: CSD304* This course aims on the concepts used in building Internet and web systems. The students would be able to understand how a web server is built.  11. Course Aims 1.    To introduce the concepts of Distributed systems, Cloud computing, web servers. 2.    To understand big data and streaming data processing on web servers. 3.    Understand concepts of naming and locating resources, directory systems, distributed data structures and applications. 4.    Apply the concepts learnt by applying them in an end-to-end project.
CSD403
Big Data and Cloud Computing
3.00
Undergraduate
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.
CSD421
Cryptography
4.00
Undergraduate
Cryptography
CSD428
Software Project Management
4.00
Undergraduate
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.
CSD429
Research Methods in Computing
4.00
Undergraduate
Prerequisite: CSD428* Foundations of Research practices, state of the art, research problem formulation, theoretical and experimental research, paper reading and writing, use of tools and techniques in research.
CSD455
Data Mining & Data Warehousing
3.00
Undergraduate
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.
CSD457
Internet of Things
3.00
Undergraduate
Internet of Things 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 IoT an important component of our daily lives. The course covers the basic concepts of IoT from system perspective and application development.
CSD459
Performance Modeling and Queuing Theory
3.00
Undergraduate
The course will enable the students to appreciate the power of analytical models in the analysis of the performance of computer communication networks.
CSD463
Wireless and Mobile Systems
3.00
Undergraduate
Course description not available
CSD464
Wireless Sensor Networks
3.00
Undergraduate
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.
CSD493
Project-1
6.00
Undergraduate
Project-1
CSD761
Advanced Data Structures and Algorithms
4.00
Graduate
Course description not available.
CSD680
Information Retrieval
4.00
Graduate
Information Retrieval
CSD211
Computer Organization and Arch
5.00
Graduate
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.
CSD654
Internet of Things
4.00
Graduate
Internet of Things
CSD700
M.Tech Thesis -1
12.00
Graduate
M.tech Thesis -1
CSD701
M. Tech Thesis-2
12.00
Graduate
M. Tech Thesis-2
CSD703
Optical Networks
3.00
Graduate
Optical Networks
CSD760
Adv. Studies in Img. Processg.
6.00
Graduate
Advanced Studies in Computer Sciences: Image Processing
CSD891
Research Methodology
4.00
Graduate
Research Methodology
CSD604
Advanced Algorithms
4.00
Graduate
Advanced Algorithms
CSD632
Machine Learning
4.00
Graduate
Machine Learning
CSD644
Advanced Computer Networks
4.00
Graduate
Advanced Computer Networks
CSD645
Cyber Physical Systems
3.00
Graduate
Cyber Physical Systems
CSD647
Wireless Sensor Networks
4.00
Graduate
Wireless Sensor Networks
CSD648
Information Theory
4.00
Graduate
Course description not available.
CSD649
Data Mining & Data warehousing
4.00
Graduate
Course description not available.
CSD651
Self Study I
4.00
Graduate
Self Study I