B.Tech Curriculum (from 2024 batch onwards)

Semester 1 Credits
MA1110 Calculus - I 1
MA1220 Calculus - II 1
EP1108 Modern Physics 2
ID1063 Programming 3
EE1030 Matrix Theory 3
CS1010 Discrete Maths for Computer Science 3
AI1001 Intro to Modern AI 1
LA1760 Communication Skills 2
Total 16


Semester 2 Credits
MA1130 Vector Calculus 1
MA1150 Differential Equations 1
MA1230 Series of Functions 1
AI1010 Introduction to Classical AI 1
AI1110 Probability and Random Variables 3
EM3020 Intro Entrepreneurship 1
AI1233 Optimization-I 3
AI1013 Programming for AI 2
LA/CA Elective 2
Total 15


Semester 3 Credits
ID2230 Data Structures & Applications 3
MA2150 Introduction to Metric Spaces 1
MA3060 Numerical Analysis 3
AI2000 Foundations of ML 3
CS3550 DBMS - I 1
EE1206 Linear Systems and Signal processing 3
AI2200 Concentration Inequalities 1
CY 1017 Environmental Chemistry 2
Total 17


Semester 4 Credits
CS2443 Algorithms 3
AI2100 Deep Learning 3
EE2101 Control Systems 3
AI2113 Optimization 2 3
LA/CA Electives 1
CS2030 Theory of Computation 3
Total 16


Semester 5 Credits
AI3000 Reinforcement Learning 3
AI4000 Robotics 3
AI3005 AI Project 3
LA/CA Electives 1
AI3013 AI for Humanity 3
XX xxxx Free Electives 3
Total 16


Semester 6 Credits
XX xxxx Free Electives 3
AI3703 Natural Language Processing 3
AI3603 Computer Vision 3
MA2570 Applied Statistics 3
LA/CA Elective 1
AI Electives 3


Semester 7 Credits
AI Electives (see baskets) 9
Internship(6 credits can be Credited from internship)
LA/CA Elective 1
Free Electives 6
Total 16


Semester 8 Credits
AI Electives (see baskets) 15
LAxxxx Ethics and Values 1
Total 16


  1. Out of 27 department electives, 15 must be from the baskets (as specified below). The remaining 12 credits can be any of the remaining basket courses.
  2. Six credits of Department Electives in the seventh semester can optinally be converted to a semester-long internship in the seventh semester. The onus is on the student to distribute/complete the remaining 9 credits in the eighth semester.
  3. Electives not in the baskets below can be considered in a given basket with the approval of faculty advisor (e.g. a new AI elective offered by a new faculty)
  4. Mini-project courses AI 3015, AI 4005, AI 4015 can be taken as followup to AI3005 in Sem 6, 7, 8 respectively as department electives.


Category Wise Split

Category Wise Split Credits Percentage
Free Electives 12 9.38%
Basic Sciences 16 12.50%
Basic Engineering Skills 16 12.50%
Departmental electives 27 21.09%
Department Core 47 36.72%
LA/CA 10 7.81%
Total 128 100.00%




“Elective Basket: Core AI and ML”
(At least 6 credits from the following)
Course Number Course Name
EE5604 Introduction to Statistical Learning Theory
EE5605 Kernel Methods for ML
AI3102 Sequence Models
CS5350 Bayesian Data Analysis
EE5470 Nonlinear Control Techniques
EE5903 Informatio Theory, coding and Inference
AI5040 Game Theory and Mechanism Design
EE5328 Introduction to Submodular Functions
AI5120 Explainability in ML
CS5470 Theory of Learning and Kernel Methods
CS5120 Probability in Computing
CS6360 Advanced Topics in Machine Learning


“Elective Basket:Speech, Vision and Language Technologies”
(At least 3 credits from the following)
Course Number Course name
CS6370 Information Retrieval
CS5700 Text Processing and Retrieval
EE6307 Speech Systems
EE6310 Image and Video Processing
CS6870 Surveillance Video Analytics
HT5083 Text-to-Speech Systems for Indian Languages
CS6803 Topics in Natual Language Processing
MA4143 Introduction to Time Series Analysis
CS6140 Video Content Analysis


“Elective Basket: Data Analytics”
(At least 3 credits from following)
Course Number Course Name
CS6890 Fraud Analytics Using Predictive and Social Network Techniques
CS5600 Data Mining
MA4143 Time Series Analysis
CS6460 Visual Big Data Analytics
CS5320 Distributed Computing
CS6713 Sclable Algorithms for data Analysis
MA4043 Algebro-Geometric Methods in Data Analysis: Theory, Applications and Algorithms
CS6070 Tensor: Techniques, Algorithms and Applications
CS3563 Introduction to DBMS II


“Elective Basket: Other Applications of AI”
(At least 3 credits from the following)
Course Number Course Name
AI5153 Mobile Robotics
BM5020 Artificial Intelligence in Biomedicine and Healthcare
BT3203 Machine Learning for Bioinformatics
BM5033 Statistical Inference Methods in Bioengineering
SM5010 Autonomous Navigation
BM6140 Theoritical and Computational Neuroscience
AI5163 Cyber Security and AI



B.Tech Curriculum (2022 & 2023 batches)

Semester 1 Credits
MA1110 Calculus - I 1
MA1220 Calculus - II 1
CY1017 Environmental Chemistry 2
EP1108 Modern Physics 2
ID1063 Introduction to Programming 3
LA1760 English Communication 2
CS1010 Discrete Math 3
AI1001 Intro to AI 1
Total 15


Semester 2 Credits
EE1203 Vector Calculus 1
MA1150 Differential Equations 1
MA1230 Series of Functions 1
AI1100 Artificial Intelligence 1
AI1110 Probability and Random Variables 3
ID1054 Digital Fabrication 2
BM1030 Bioengineering 2
AI1104 Programming for AI 1
LA/CAxxxx LA/CA Elective 3
Total 15


Semester 3 Credits
ID2230 Data Structures 3
MA2150 Introduction to Metric Spaces 1
CS2323 Computer Architecture 2
CS3510 OS - I 1
EE2100 Matrix Theory 3
CS3550 DBMS - I 1
EE1206 Linear Systems and Signal processing 3
LA/CAxxxx LA/CA Elective 1
LA1770 Personality Development 1
Total 16


Semester 4 Credits
AI2101 Convex Optimization 3
CS2443 Algorithms 3
AI2000 Foundations of Machine Learning 3
MA4240 Applied Statistics 3
CS3320 Compilers - I 1
CS3563 DBMS - II 3
EM3020 Intro to Entrepreneurship 1
Total 17


Semester 5 Credits
MA5060 Numerical Analysis 3
AI3000 Reinforcement Learning 3
AI2200/AI20300 Concentration Inequalities 1
AI2100 Deep Learning 3
AI4000 Robotics 3
EE2101 Control Systems 3
LA xxxx LA/CA Elective 1
Total 17


Semester 6 Credits
XX xxxx Free Electives 6
AI3703/AI37003 Natural Language Processing 3
AI3603/AI36003 Computer Vision 3
AI elective 3
LA/CA Elective 1
Total 16


Semester 7 Credits
AI Electives (see baskets) 9
Internship(6 credits can be credited from internship)
Free Electives 6
Total 15


Semester 8 Credits
AI Electives (see baskets) 15
LAxxxx Ethics and Values 1
Total 16



Category Wise Split


Category Wise Split Credits Percentage
Free Electives 12 9.38%
Basic Sciences 16 12.50%
Basic Engineering Skills 16 12.50%
Departmental electives 27 21.09%
Department Core 48 37.50%
LA/CA 9 7.03%
Total 128 100.00%




Minor in AI


Students have to finish a total of 12 credits, with at least one course from each of the categories(rows) below. If a student has already completed some of these categories as part of the regular B.Tech Program, the student should take an equivalent number of credits to compensate.

Courses Category
AI2000 (or) AI5000(or) CS3390 (or) EE2802 Foundations of Machine Learning
AI2100 (or) AI5100 Deep Learning
List below Electives

Note: Electives not in the given bhasket lists can be considered in a given basket with approval of faculty advisor (e.g. a new AI elective offered by a new faculty).

List of Electives

Course Number Course Name
EE5604 Intro to Statistical Learning Theory
EE5605 Kernal Methods for ML
AI3102 Sequence Methods
CS5350 Bayesian Data Analysis
EE5470 Nonlinear Control Techniques
EE5903 Information Theory, Coding and Inference
AI5040 Game Theory and Mechanism Design
EE5328 Introduction to Submodular Functions
AI5120 Explainability in ML
CS5470 Theory of Learning and Kernel Methods
CS5120 Probability in Computing
CS6360 Advanced Topics in Machine Learning
AI5153 Mobile Robotics
BM5020 Artificial Intelligence in Biomedicine and Healthcare
BT3203 Machine Learning for Bioinformatics
BM5033 Statistical Inference Methods in Bioengineering
SM5010 Autonomous Navigation
BM6140 Theoretical and Computational Neuroscience
CS6370 Information Retrieval
CS5700 Text Processing and Retrieval
EE6307 Speech Systems
EE6310 Image and Video Processing
CS6870 Survaillance Video Analysis
HT5083 Text-to-Speech Systems for Indian Languages
CS6803 Topics in Natural Language Processing
MA4143 Introuction to Time Series Analysis
CS6140 Video Content Analysis
CS6890 Fraud Analytics Using Predictive and Social Network Techniques
CS5600 Data Mining
MA4143 Time Series Analysis
CS6460 Visual Big Data Analysis
CS5320 Distributed Computing
CS6713 Scalable algorithms for data analysis
CS6713 Scalable algorithms for data analysis
MA4043 Algebro-Geometric Methods in Data Analysis: Theory, Applications and Algorithms
CS6070 TensTensors: Techniques, Algorithms and Applications
CS3563 Introuction to DBMS II
AI5163 Cybersecurity and AI

B.Tech Curriculum (from 2021 batch onwards)

Semester 1 Credits
MA1110 Calculus-I 1
MA1220 Calculus-II 1
CY1017 Environmental Chemistry 2
EP1108 Modern Physics 2
ID1063 Introduction to Programming 3
SSxxxx English Communication 2
CS1010 Discrete Math 3
AI1001 Intro to AI 1
Total 15


Semester 2 Credits
EE1203 Vector Calculus 1
MA1150 Differential Equations 1
MA1230 Series of Functions 1
AI1100 Artificial Intelligence 2
AI1110 Probability and Random Variables 3
ID1054 Digital Fabrication 2
BM1030 Bioengineering 2
AI1104 Programming for AI 1
LA/CAxxxx LA/CA Elective 3
Total 16


Semester 3 Credits
ID2230 Data Structures 3
MAxxxx Introduction to Metric Spaces 1
AI2000 Foundations of Machine Learning 3
EE2100 Matrix Theory 3
CS3550 DBMS - I 1
EE3900 Linear Systems and Signal processing 3
LA/CA LA/CA Elective 1
SSxxxx Personality Development 1
Total 16


Semester 4 Credits
AI2101 Convex Optimization 3
CS2443 Algorithms 3
AI2100 Deep Learning 3
MA4240 Applied Statistics 3
CS3320 Compilers - I 1
CS3563 DBMS - II 3
SSxxxx Intro to Entrepreneurship 1
Total 17


Semester 5 Credits
MA5060 Numerical Analysis 3
CS2323 Computer Architecture 2
AI3000 Reinforcement Learning 3
AI3020 Intro to Computer Networks 1
CS3510 OS - I 1
AI3001 Advanced Topics in ML 2
EE2102 Control Systems 3
LA/CAxxxx LA/CA Elective 1
Total 16


Semester 6 Credits
XXxxxx Free Electives 6
AI Electives
(6 credits can be converted to internship) 		9
IDxxxx Engineering Electives 1
Total 16


Semester 7 Credits
AI Electives (see baskets) 9
LA/CAxxxx LA/CA Elective 1
AI4000 Robotics 3
AI4013 AI for Humanity 3
Total 16


Semester 8 Credits
AI Electives (see baskets) 9
XXxxxx Free Electives 6
LAxxxx Ethics and Values 1
Total 16



Category Wise Split


Category Wise Split Credits Percentage
Free Electives 12 9.38%
Basic Sciences 16 12.50%
Basic Engineering Skills 16 12.50%
Soft Skills 4 3.13%
Department Core 73 57.03%
LA/CA 7 5.47%
Total 128 100.00%



Elective Baskets


  • Out of 30 department electives, 17 must be from the baskets (as specified above). The remaining 13 credits can be any of the remaining basket courses or any CS/EE/MA courses.
  • At least TWO of these department elective courses must be 3-credit courses.
  • Six credits of Department Electives in the sixth semester can optionally be converted to a semester long internship in the sixth semester. The onus is on the student to distribute/complete the remaining 10 credits in the sixth semesters in other semesters.
  • Maximum 4 credit of CA courses, and 6 credits of LA courses can be taken.
  • For most AI courses, the lab component is built into the courses themselves.
  • Electives not in the lists below can be considered in a given basket with approval of faculty advisor (e.g. a new AI elective offered by a new faculty).
“Core AI and ML”
(At least 3 credits from the following)
Course Name Credits
Intro to Statistical Learning Theory 1
Kernel Methods 1
Sequence Models
Optimization Methods in Machine Learning / Convex Optimization-II 3
Bayesian Data Analysis 2
Nonlinear Control Techniques 3
Information Theory and Coding 3
Introduction to Submodular Functions 1
Game Theory and Mechanism Design 3


“Language Technologies”
(At least 3 credits from the following)
Course Name Credits
Natural Language Processing 3
Information Retrieval 3
Text Processing 3


“Speech and Vision”
(At least 3 credits from the following)
Course Name Credits
Computer Vision 3
Speech Systems 3
Image and Video Processing 3
Surveillance Video Analytics 3


“Data Analytics”
(At least 3 credits from following)
Course Name Credits
Predictive Analysis 3
Data Mining 3
Time Series Analysis 1
Graph Analytics for Big Data 3
Distributed Systems 3
Cloud Computing 3
Big Data: Tools and Techniques 1


“AI, Neuroscience and Natural Intelligence”
(At least 3 credits from the following)
Course Name Credits
Computational Neuroscience Lab 2
Brain Machine Interfaces 3
Movement Sciences Lab 2
Movement Science and Disorders 3
Theoretical & Computational
Neuroscience 		2
Applications of AI in Healthcare 1