| Department of Computer Science and Engineering

B.Tech. in Computer Science & Engineering

Core Credits includes Core Course Credits (61) + Basic Science Credits (17) + Engineering Science Credits (13) + Project Credits (12).

Total Credits


Core Credits


Major Electives


CCC + UWE credits

Core & Elective Courses

Core Courses

Core courses are compulsory courses that provide critical foundations to the undergraduate program. These courses are mandatory in order to develop in-depth knowledge of the discipline.

Course code
Introduction to Computing and Programming

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.

Data Structures

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.

Operating Systems

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.

Discrete Mathematics

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

Introduction to Probability and Statistics

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.

Computer Organization and Arch

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.

Object Oriented Programming

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.

Computer Networks

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.

Artificial Intelligence

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.

Introduction to Database Systems

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.

Design and Analysis of Algorithms

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.

Software Engineering

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.

Theory of Computation

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.

Software Design Lab

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


Students will learn to read, understand and present latest research papers in their chosen area of interest.





Intro. to Electrical Engg.

Circuit Analysis Review of KCL and KVL, Basic Circuit Terminology-Node, loop, mesh, circuit, branch and path. Ideal sources, Source transformation, Star-Delta transformation. AC analysis - Phasor, Complex impedance, complex power, power factor, power triangle, impedance triangle, series and parallel circuits
Network Theorems
Network Theorems (A.C. and D.C Circuits) - Mesh and Nodal analysis, Thevenin, Norton, Maximum Power transfer, Millman, Tellegen and Superposition theorem.
Resonance and Transient Analysis
Introduction to Resonance-series and parallel, half power frequency, resonant frequency, Bandwidth, Q factor. Transient Analysis-Step response, Forced Response of RL, RC & RLC Series circuits with Sinusoidal Excitation – Time Constant & Natural frequency of Oscillation – Laplace Transform applications.
Electronic Devices and Components
Review of Energy band diagram- Intrinsic and Extrinsic semiconductors- PN junction diodes and Zener diodes – characteristics, Diode Applications-Rectifiers, Clippers and Clampers. Transistors-PNP and NPN – operation, characteristics and applications, Biasing of Transistors. Operational Amplifiers-Introduction and Applications - Inverting, Non Inverting, Voltage follower, Integrator, differentiator and difference amplifier, Summer, log and Antilog.
Three Phase and Transformers Introduction to three phase, power measurements in three phase. Transformer-Principle of operation, construction, phasor diagram of Ideal and practical transformer with load (R,L,C and their combinations) and no load, equivalent circuit, efficiency and voltage regulation of single phase transformer, O.C. and S.C. tests. Introduction to D.C. Machines.

Signals and Systems

1. Classification and representation of signals and systems, Continuous time & Discrete time signals and systems, Impulse and Step response of a system, linear systems, linearity, time invariance, causality, signal properties -LTI systems, Convolution
2. Fourier series, Fourier transform and properties, relation between Fourier transform and Fourier series, Sampling and reconstruction, FFT, DIT FFT, DIF FFT Algorithm, Inverse DFT and Convolution using FFT
3. Laplace transforms- representation of signals using continuous time complex exponentials, relation of Laplace and Fourier transform, concept of ROC and transfer function- block diagram representation, Inverse Laplace transform, properties, analysis and characterization of LTI systems using Laplace transforms
4. Z transforms- representation of signals using discrete time complex exponentials-properties, inverse Z transforms, ROC, Analysis and characterization of LTI systems using Z transforms, block diagram, transfer functions
5. Introduction to random variable and random process, State space analysis, Introduction to Two port networks and parameters

Digital Electronics

Digital Processing of Information – Basic information processing steps – logic and arithmetic; Number Systems and Arithmetic – Positional number systems, Arithmetic operations on binary numbers; Combinational Logic – Basic logic operations, Boolean algebra, Boolean functions, De Morgan’s laws, Truth table and Karnaugh map representations of Boolean functions, Combinational circuit design using gates and multiplexers; Sequential Logic – Latches and Flip-flops, Ripple counters, Sequence generator using flip-flops, State Diagram, Synchronous counters, Shift Registers; Introduction to the Microprocessor – Basic constituents of a processor, Instruction set – machine language and assembly language.

Mathematical Methods I

Core course for all B.Tech. Optional for B.Sc. (Research) Chemistry. Not open as UWE.

Credits (Lec:Tut:Lab)= 3:1:0 (3 lectures and 1 tutorial weekly)

Prerequisites: Class XII Mathematics.

Overview:  In this course we study multi-variable calculus. Concepts of derivatives and integration will be developed for higher dimensional spaces. This course has direct applications in most engineering applications. 

Detailed Syllabus:

  1. Review of high school calculus.
  2. Parametric curves (Vector functions): plotting, tangent, arc-length, polar coordinates, derivatives and integrals.                                                                    
  3. Functions of several variables: level curves and surfaces, differentiation of functions of several variables, gradient, unconstrained and constrained optimization.
  4. Double and triple integrals: integrated integrals, polar coordinates, cylindrical and spherical coordinates, change of variables.
  5. Vector fields, divergence and curl, Line and surface integrals, Fundamental Theorems of Green, Stokes and Gauss.


  1. A Banner, The Calculus Lifesaver, Princeton University Press.
  2. James Stewart, Essential Calculus – Early Transcendentals, Cengage.
  3. G B Thomas and R L Finney, Calculus and Analytic Geometry, Addison-Wesley.
  4. Erwin Kreyszig, Advanced Engineering Mathematics, Wiley.

Past Instructors: Ajit Kumar, Sneh Lata

Applied Linear Algebra

Course description not available.

Materials Science & Engg.

Chapter-1: Introduction Material science and engineering, Classification of engineering materials, Structureproperty relationship, Bonding forces and energies, Equilibrium and kinetics, Stability and Meta-stability, Basic thermodynamic functions, Entropy, Kinetics of thermally activated processes
Chapter-2: Crystal Geometry and Structure Determination Geometry of crystal, Space lattice, Crystal structure, Crystal directions and planes, Structure determination by X-ray diffraction, atomic structure and chemical bonding Chapter-3: Crystal Imperfections Defects in materials, Point defects, Dislocations, Properties of dislocations, Dislocation theory Surface imperfections
Chapter-4: Phase Diagrams The phase rule, Single-component systems, Binary-phase diagrams, Iron-Carbon Phase diagram, Microstructural changes during cooling, The lever rule.
Chapter-5: Phase Transformations
Time-scale for phase change, Nucleation and grain growth, Nucleation kinetics, Overall transformation kinetics, Applications, Recovery, recrystallization and grain growth, Diffusion
Chapter-6: Plastic Deformation in Crystalline Materials Plastic deformation by slip, Shear strength of perfect and real crystals, Critical resolved shear stress for slip, Stress to move a dislocation, Effect of temperature on dislocation movement, Dislocation multiplication, Work hardening and dynamic recovery
Chapter-7: Strengthening Mechanisms in Materials Introduction, strengthening from grain boundaries, Solid solution strengthening, strengthening by fine particles, Strain hardening, Bauschinger effect
Chapter-8: Material Properties Concept of stress and strain, True stress and strain, Compressive, shear and torsional deformation, Hardness, Ductile and brittle fracture, Cyclic stresses, S-N Curve

Introduction to Physics I

The aim of this course is to bridge the gap between the various boards across the country at 10+2 level and bring everyone at the standard undergraduate level. All the engineering branches have their origin in the basic physical sciences. In this course we aim to understand the basic physical laws and to develop skills for application of various physical concepts to the science and engineering through problem solving. This will involve the use of elementary calculus like differentiation and integration.   

Detailed Syllabus        

Mechanics: The inertial reference frames, Newton’s laws of motion in vector notation, Conservation of energy, Application of Newton’s laws of motion, Dynamical stability of systems: Potential energy diagram, Collisions: Impulse, conservation of energy and linear, momentum, Conservation of angular momentum and rotation of rigid bodies in plane Thermal Physics: Averages, probability and probability distributions, Thermal equilibrium and macroscopic variables, Pressure of an ideal gas from Newton’s laws - the kinetic theory of gases. Maxwell’s velocity distribution, Laws of Thermodynamics and the statistical origin of the second law of thermodynamics, Application of thermodynamics: Efficiency of heat engines and air-conditioners, Thermodynamics of batteries and rubber bands

Introduction to Physics II

This is a continuation of PHY 101 and is meant for engineers and non-physics majors. The course will introduce students to Electricity and Magnetism, Maxwell’s equations, Light as an electromagnetic wave, and Wave optics. 
Vector calculus: Gradient, Divergence, Curl and fundamental theorems of vector calculus. Basic laws in electricity and magnetism, Classical image problem, displacement current and continuity equation, Maxwell’s Equations, electromagnetic wave equation and its propagation in free space, conducting media and dielectric medium, Poynting theorem, Electromagnetic spectrum. 
Wave Optics: 
Interference of light waves: Young’s double slit experiment, displacement of fringes, Interference in thin films 
Diffraction: Fresnel’s and Fraunhofer’s class of diffraction, diffraction from single, double & N- Slits, Gratings. 
Polarization: Concept of Polarization in electromagnetic waves, types of polarized waves.

Elective Courses

The department currently has 3 identified areas of growth, and students are expected to work with faculty in one of these areas. These 3 areas and some of their sub-areas of research are mentioned below:

Artificial Intelligence and Machine Learning: Natural Language Processing, Introduction to Machine Learning, Computer Vision and Deep Learning

Data Science and Big Data Analytics: Data Mining and Warehousing, Information Retrieval, Introduction to Machine Learning, Algorithms for Big Data and Big Data Analytics.

Cyber Security and Privacy: Foundation of Information Security, Applied Cryptography, Internet of Things, Wireless and Mobile Systems and Wireless Sensor Networks.

Course code
Natural Language Processing

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.

Advanced Database Mgmt Systems

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.

Computational Neuroscience

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.

Computer Graphics

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.

Foundations of Data Science

Course description not available

Foundation of Information Security

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

Image Processing & its Apps.

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,

Information Retrieval

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.

Introduction to Logic and Functional Programming

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.

Introduction to Machine Learning

The course introduces the basic concepts, techniques and tools for designing programs that learn from data.

Social and Information Networks

Course description not available.

Algorithms for Big Data

Course description not available.

Applied Cryptography

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.

Big data Analytics

Course description not available.

Computer Vision

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.

Data Mining & Data Warehousing

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.

Deep Learning

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.

Internet of Things

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.

Introduction to Geometric Algorithms

Course description not available.

Performance Modeling and Queuing Theory

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

Virtualization and Cloud Computing

Course description not available.

Wireless and Mobile Systems

Course description not available

Wireless Sensor 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.

Special Topics in Artificial Intelligence

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

Special Topics in Applications

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

Special Topics in Systems

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

Special Topics in Theoretical in Computer Science

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

Special Module in Artificial Intelligence

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.

Special Module in Applications

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.

Special Module in Systems

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.

Special Module in Theoretical Computer Science

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.