B.Tech in Computer Science and Artificial Intelligence

  • Artificial Intelligence (AI) has become an integral part of technology in our daily lives, driving to office, searching for a restaurant, getting news updates, and recommendations on social media are all using AI. With increase in usage, there is a significant requirement of researchers who can understand AI and build AI technologies. This program will provide students an opportunity to learn both foundational and experimental components of AI and Machine Learning.

  • A student completing this program will be able to undertake industry careers involving innovation and problem solving using Artificial Intelligence (AI) and Machine Learning (ML) technologies and research careers in AI, ML, and, in general, Computer Science areas. Along with courses that provide specialization in AI, students will also have option to explore applied domains such as computer vision, natural language processing, robotics, and autonomous systems as well as other interdisciplinary areas such as neuroscience, edge computing, and Internet of Things.

Objectives


At the end of this program, a student should have:

  • » Understanding of foundational topics in Computer Science, Artificial Intelligence, and Machine Learning.
  • » Understanding of theoretical foundations and limits of Artificial Intelligence and Machine Learning.
  • » Ability to design and implement algorithms and data structures for efficiently solving new problems.
  • » Ability to model and analyze a variety of problems using appropriate mathematical/computational and AI concepts.
  • » Ability of apply and develop AI algorithms to transform large amount of data into intelligent decisions and/or behavior.
  • » An understanding of the impact of AI based solutions in an economic, societal, and environment context.

Some of the salient points of this program are:

  • » As it is an AI-ML focused program, it "inverts the pyramid" and start with computing and basic AI oriented courses first followed by AI-ML application related courses along with other open electives. Besides being better suited for an AI program, it also enables the possibility of students seeing newer applications and possibilities of relating AI-ML with these subjects.
  • » Has a highly structured core AI program that focuses on Computer Science and Artificial Intelligence fundamentals as well as communication skills.
  • » After the core program, there is flexibility on the AI courses the students can choose for developing the skills and knowledge in various topics - computing and AI application domains.
  • » Humanities and social sciences is an integral part of the curriculum, particularly ethical aspects of AI.
  • » Allows motivated students to graduate with a "BTech (Honors)" by doing extra units.
  • » Has a heavy emphasis on hands-on practice with recent tools and technology as part of lab and programming assignments and courses projects.
  • » Builds research skills through courses like "independent study", "independent projects" and "undergraduate research" along with undergraduate thesis.

Semester-wise plan (152 Credits)

(This is a tentative plan which a student can follow as per pre-req requirements and minimum credit per semester requirements - 16 or 20 credits per semester)


Total minimum credits for AI related courses is 48 credits (12 courses)

SEM 1
(Common for all programs)

SEM 2

SEM 3

SEM  4

SEM 5

SEM 6

SEM 7

SEM 8

Introduction to Programming

Data Structures and Algorithms

Advanced Programming

Optimization

ML

 

 

 

Digital Circuits

Introduction of Intelligent Systems

OS

Ethics in AI / DBMS/CO

CA/CN/Compilers

Ethics in AI

 

 

Maths I-Linear Algebra

Maths II-Probability & Statistics

Discrete Mathematics

Algorithm Design and Analysis

AI

2 AI Core Courses

System Management+IED

CO/DBMS

Signals & Systems

MIV/GT/TOC/SI/IML

AI Application Electives (4 courses)

Communication Skills

SSH

Maths III

SML

Technical Comm + Environmental Sciences

 

 

 

28 credits are free electives in between Sem 5 to 8 (they are represented as blank slots)

Color coding:

  • » Red: compulsory courses
  • » Green: core but with options to do as per the choices
  • » Blue: AI core and applications where students have choices

In between Semesters 5 - 8, students also need to do one SSH course


Requirements for Graduation

For a B.Tech. (CSAI) degree, a student must satisfy all the following requirements:

  • » Earn a total of 156 credits (including 2 credits each of SG and CW credits) - one full course counts towards 4 credits
  • » Earn 68 credits of compulsory courses - including AI compulsory courses
  • » Earn 8 credits of additional CS core courses and 4 credits of Maths Elective Courses
  • » Earn 24 credits of additional AI courses. Along with these, Ethics in AI (4 cr - SSH) is a required course.
  • » BTech Project (BTP) is optional. A student opting for BTP, may take a total of 8 to 12 credits of BTP.
  • » A student may take "Independent Project" or "Independent Study" or "Undergraduate Research" courses for 1, 2, or 4    credits. No more than 8 of these credits can count towards satisfying the credit requirements of the degree. Only students    with satisfactory CGPA (at least 7.5) or with a strong interest in some area (the faculty advisor to determine this) can take        these courses.

Honors Program

  • » The student must earn an additional 12 credits (i.e. must complete at least 168 credits)
  • » The student's program must include a BTech Project in AI domain
  • » At graduation time, the student must have a CGPA of 8.0 or more

Courses

BTech CSAI program can be graphically represented as follows:

BTECH CSAI PROGRAM

A. Compulsory (17 X 4 = 68 credits)

- CS related Compulsory courses (6 X 4 = 24 credits): (i) Intro to Programming, (ii) Data Structure and Algorithms, (iii) Discrete Mathematics, (iv) Advanced Programming, (v) Analysis and Design of Algorithms, (vi) Operating Systems - ECE/HCD related Compulsory courses (3 X 4 = 12 credits): (i) Digital Circuits, (ii) Introduction to Engineering Design + SM, (iii) Signal & Systems - Maths relate Compulsory Courses (4 X 4 = 16 credits): (i) Math-1, (ii) Math-2, (iii) MTH 3, and (iv) Optimization - AI Compulsory (4 X 4 = 16 credits): (i) Introduction of Intelligent Systems (a basic course on start with Turing machine and then giving a summary of all areas in AI domain - in 2nd semester), (ii) Statistical Machine Learning (Pattern Recognition), (iii) Machine Learning, (iv) Artificial Intelligence


B. Additional CS Core Courses: Select 2 out of following courses (2X4 = 8 credits) - should be done in first 4-semesters

- Computer Architecture - Computer Organization - Fundamentals of Database Management Systems - Computer Networks - Compilers


C. Additional AI Core Courses: Select 2 out of the following courses (2 X 4 = 8 credits)

- Deep Learning - Advanced Machine Learning - Reinforcement Learning - Data Mining - Big Data Analytics - Data Science - Probabilistic Graphical Models - Human-AI Interaction


D. AI Applications Courses: Select 4 out of the following courses (4 X 4 = 16 credits)

- Computer Vision - Natural Language Processing - Information Retrieval - Robotics - Multi-agent Systems - Collaborative Filtering/Recommender Systems - Speech Recognition and Understanding - Semantic Web/Knowledge Graphs - Additional Applications Related Courses


E. MTH Elective Courses: Select 1 out of the following: (1 X 4 = 4 credits)

- MTH 4 - Scientific Computing - Graph Theory - Statistical Inference - Theory of Computation - Introduction to Mathematical Logic


F. SSH Courses (5 X 4 = 20 credits):
(i) (TCOM+EVS), (ii) TCOMM, (iii) Ethical, Social, and Legal (ESL) Aspects in AI, and 2 other HSS course with recommendation for (a) Game Theory, (b) Critical Thinking.


G. Open Electives
(including up to 12 units of BTP and 8 units of Independent Project/Undergraduate Research/Independent Study): Any of the courses from above and electives from other departments (7 X 4 = 28 credits)

  1. 1. Option 1: Students can select any course of their choice
  2. 2. Option 2: Give them a guideline to choose open elective courses related to some domains (more can be added):
    •      » Neuroscience or Cognition
    •      » AI in Heathcare
    •      » Hardware related AI, Edge Computing and AI
    •      » Parallel or Distributed AI for large scale applications
    •      » Human Centered AI
    •      » IoT and AI