B.Tech Curriculum
| Curriculum for B.Tech in Artificial Intelligence (2025 onwards) | ||||||
|---|---|---|---|---|---|---|
| Year | Odd Semester | Credits | Even Semester | Credits | ||
| 1 | MA1110 | Calculus I | 1 | MA1150 | Differential Equations | 1 |
| MA1220 | Calculus II | 1 | MA1230 | Series of Functions | 1 | |
| Science Basket (See below) | 2 | Science Basket (See below) | 1 | |||
| ID1063 | Programming | 3 | AI1233 | Optimization - I | 3 | |
| AI1000 | Matrix Theory | 3 | AI1104 | Programming for AI | 2 | |
| CS1010 | Discrete Math for Computer Science | 3 | AI1100 | Intro to Classical AI | 1 | |
| AI1001 | Intro to Modern AI | 1 | AI1110 | Probability and Random Variables | 3 | |
| LA1760 | Communication Skills | 2 | EM3020 | Intro to Entrepreneurship | 1 | |
| LA/CA Elective | 2 | |||||
| Total | 16 | Total | 15 | |||
| 2 | MA2150 | Introduction to Metric Spaces | 1 | AI2100 | Deep Learning | 3 |
| MA5060 | Numerical Analysis | 3 | AI2113 | Optimization - II | 3 | |
| Science Basket (See below) | 2 | CS2443 | Algorithms | 3 | ||
| CS3550 | DBMS - I | 1 | AI3013 | AI for Humanity | 3 | |
| ID2230 | Data Structures & Applications | 3 | EE2101 | Control Systems | 3 | |
| EE1206 | Linear Systems and Signal Processing | 3 | LA/CA Electives | 1 | ||
| AI2200 | Concentration Inequalities | 1 | ||||
| AI2000 | Foundations of ML | 3 | ||||
| Total | 17 | Total | 16 | |||
| 3 | Free Elective | 3 | Free Elective | 3 | ||
| AI3000 | Reinforcement Learning | 3 | MA2570 | Applied Statistics | 3 | |
| AI4000 | Robotics | 3 | AI3703 | Natural Language Processing | 3 | |
| AI3005 | AI Project | 3 | AI3603 | Computer Vision | 3 | |
| AI Elective | 3 | AI Elective | 3 | |||
| LA/CA Electives | 1 | LA/CA Elective | 1 | |||
| Total | 16 | Total | 16 | |||
| 4 | Free Elective | 6 | AI Electives (see baskets) | 15 | ||
| Internship* | LAxxxx | Ethics and Values | 1 | |||
| AI Electives (see baskets) | 9 | |||||
| LA/CA Elective | 1 | |||||
| Total | 16 | Total | 16 | |||
*(6 credits can be credited from internship)
- 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.
- Six credits of Department Electives in the seventh semester can optionally 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 eighthย semester.
- Electives not in the baskets below can be considered in a given basket with the approval of faculty advisor (e.g. aย new AI elective offered by a new faculty).
- 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 | Credits | Percentage |
|---|---|---|
| Free Elective | 12 | 9.38% |
| Basic Science | 16 | 12.50% |
| Basic Engineering Skills | 16 | 12.50% |
| Department Elective | 30 | 23.44% |
| Department Core | 44 | 34.38% |
| LA/CA | 10 | 7.81% |
| Total | 128 | 100% |
| Science Basket | |
|---|---|
| Course Code | Course Name |
| EP1108 | Modern Physics |
| CY1010 | Environmental Chemistry |
| MA2130 | Complex Variables |
| MA2120 | Transform Techniques |
| MA2150 | Introduction to Metric Spaces* |
| EP2200 | Thermodynamics* |
| EP2108 | Special Relativity* |
| EP2228 | Fluid Dynamics* |
| EP2100 | Classical Mechanics* |
| CC1010 | Introduction to Climate change* |
| MA1240 | Combinatorics* |
| MA2140 | Introduction to Statistics* |
| MA2510 | Linear Programming* |
*Subject to approval of parent department
| Curriculum for B.Tech in Artificial Intelligence (2024) | ||||||
|---|---|---|---|---|---|---|
| Year | Odd Semester | Credits | Even Semester | Credits | ||
| 1 | MA1110 | Calculus I | 1 | MA1130 | Vector Calculus | 1 |
| MA1220 | Calculus II | 1 | MA1150 | Differential Equations | 1 | |
| EP1108 | Modern Physics | 2 | MA1230 | Series of Functions | 1 | |
| ID1063 | Programming | 3 | AI1233 | Optimization - I | 3 | |
| EE1030 | Matrix Theory | 3 | AI1104 | Programming for AI | 2 | |
| CS1010 | Discrete Math for Computer Science | 3 | AI1100 | Intro to Classical AI | 1 | |
| AI1001 | Intro to Modern AI | 1 | AI1110 | Probability and Random Variables | 3 | |
| LA1760 | Communication Skills | 2 | EM3020 | Intro to Entrepreneurship | 1 | |
| LA/CA Elective | 2 | |||||
| Total | 16 | Total | 15 | |||
| 2 | MA2150 | Introduction to Metric Spaces | 1 | AI2100 | Deep Learning | 3 |
| MA5060 | Numerical Analysis | 3 | AI2113 | Optimization - II | 3 | |
| CY1010 | Environmental Chemistry | 2 | CS2443 | Algorithms | 3 | |
| CS3550 | DBMS - I | 1 | AI3013 | AI for Humanity | 3 | |
| ID2230 | Data Structures & Applications | 3 | EE2101 | Control Systems | 3 | |
| EE1206 | Linear Systems and Signal Processing | 3 | LA/CA Electives | 1 | ||
| AI2200 | Concentration Inequalities | 1 | ||||
| AI2000 | Foundations of ML | 3 | ||||
| Total | 17 | Total | 16 | |||
| 3 | Free Elective | 3 | Free Elective | 3 | ||
| AI3000 | Reinforcement Learning | 3 | MA2570 | Applied Statistics | 3 | |
| AI4000 | Robotics | 3 | AI3703 | Natural Language Processing | 3 | |
| AI3005 | AI Project | 3 | AI3603 | Computer Vision | 3 | |
| AI Elective | 3 | AI Elective | 3 | |||
| LA/CA Electives | 1 | LA/CA Elective | 1 | |||
| Total | 16 | Total | 16 | |||
| 4 | Free Elective | 6 | AI Electives (see baskets) | 15 | ||
| Internship* | LAxxxx | Ethics and Values | 1 | |||
| AI Electives (see baskets) | 9 | |||||
| LA/CA Elective | 1 | |||||
| Total | 16 | Total | 16 | |||
*(6 credits can be credited from internship)
- 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.
- Six credits of Department Electives in the seventh semester can optionally 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 eighthย semester.
- Electives not in the baskets below can be considered in a given basket with the approval of faculty advisor (e.g. aย new AI elective offered by a new faculty).
- 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 | Credits | Percentage |
|---|---|---|
| Free Elective | 12 | 9.38% |
| Basic Science | 16 | 12.50% |
| Basic Engineering Skills | 16 | 12.50% |
| Department Elective | 30 | 23.44% |
| Department Core | 44 | 34.38% |
| LA/CA | 10 | 7.81% |
| Total | 128 | 100% |
| Curriculum for B.Tech in Artificial Intelligence (2022 & 2023 Batch) | ||||||
|---|---|---|---|---|---|---|
| Year | Odd Semester | Credits | Even Semester | Credits | ||
| 1 | MA1110 | Calculus I | 1 | EE1203 | Vector Calculus | 1 |
| MA1220 | Calculus II | 1 | MA1150 | Differential Equations | 1 | |
| EP1108 | Modern Physics | 2 | MA1230 | Series of Functions | 1 | |
| CY1010 | Environmental Chemistry | 2 | BM1030 | Bioengineering | 2 | |
| ID1063 | Programming | 3 | AI1104 | Programming for AI | 1 | |
| CS1010 | Discrete Math for Computer Science | 3 | ID1054 | Digital Fabrication | 2 | |
| AI1001 | Intro to AI | 1 | AI1100 | Artificial Intelligence | 1 | |
| LA1760 | English Communication | 2 | AI1110 | Probability and Random Variables | 3 | |
| LA/CA Elective | 3 | |||||
| Total | 15 | Total | 15 | |||
| 2 | MA2150 | Introduction to Metric Spaces | 1 | MA4240 | Applied Statistics | 3 |
| EE1206 | Linear Systems and Signal Processing | 3 | CS3320 | Compilers - I | 1 | |
| ID2230 | Data Structures | 3 | AI2101 | Convex Algorithms | 3 | |
| CS2323 | Computer Architecture | 3 | CS2443 | Algorithms | 3 | |
| CS3550 | DBMS - I | 1 | AI2000 | Foundations of Machine Learning | 3 | |
| CS3510 | OS - I | 1 | CS3563 | DBMS - II | 3 | |
| EE2100 | Matrix Theory | 3 | EM3020 | Intro to Entrepreneurship | 1 | |
| LA1770 | Personality Development/LA/CA Elective | 1 | ||||
| LA/CA Elective | 1 | |||||
| Total | 17 | Total | 17 | |||
| 3 | MA5060 | Numerical Analysis | 3 | Free Elective | 6 | |
| EE2101 | Control Systems | 3 | AI3703 | Natural Language Processing | 3 | |
| AI2100 | Concentration Inequalities | 1 | AI3603 | Computer Vision | 3 | |
| AI3000 | Reinforcement Learning | 3 | AI Elective | 3 | ||
| AI4000 | Robotics | 3 | LA/CA Elective | 1 | ||
| AI2100 | Deep Learning | 3 | ||||
| LA/CA Electives | 1 | |||||
| Total | 17 | Total | 16 | |||
| 4 | Free Elective | 6 | AI Electives (see baskets) | 15 | ||
| Internship* | LAxxxx | Ethics and Values | 1 | |||
| AI Electives (see baskets) | 9 | |||||
| Total | 15 | Total | 16 | |||
*(6 credits can be credited from internship)
- 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.
- Six credits of Department Electives in the seventh semester can optionally 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 eighthย semester.
- Electives not in the baskets below can be considered in a given basket with the approval of faculty advisor (e.g. aย new AI elective offered by a new faculty).
| Category | Credits | Percentage |
|---|---|---|
| Free Elective | 12 | 9.38% |
| Basic Science | 16 | 12.50% |
| Basic Engineering Skills | 16 | 12.50% |
| Department Elective | 27 | 21.09% |
| Department Core | 46 | 35.94% |
| LA/CA | 11 | 8.59% |
| Total | 128 | 100% |
| Basket: Core AI and ML (At least 6 credits from the following) | |
|---|---|
| Course Code | Course Name |
| AI3102 | Sequence models |
| AI5040 | Game Theory and Mechanism Design |
| AI5120 | Explainability in ML |
| CS5350 | Bayesian Data Analysis |
| CS5470 | Theory of Learning and Kernel Methods |
| CS5120 | Probability in Computing |
| CS6360 | Advanced topics in Maching Learning |
| EE5604 | Intro to Statistical Learning theory |
| EE5605 | Kernel Methods for ML |
| EE5470 | Nonlinear Control Techniques |
| EE5903 | Information Theory, Coding and Inference |
| EE5328 | Introduction to Submodular Functions |
| Basket: Data Analytics (At least 3 credits from following) | |
|---|---|
| Course Code | Course Name |
| CS6890 | Fraud Analytics Using Predictive and Social Network Techniques |
| CS5600 | Data Mining |
| CS6460 | Visual Big Data Analytics |
| CS5320 | Distributed Computing |
| CS6713 | Scalable algorithms for data analysis |
| CS6070 | Tensor: Techniques, Algorithms and Applications |
| CS3563 | Introduction to DBMS II |
| MA4143 | Introduction to Time Series Analysis |
| MA4043 | Algebro-Geometric Methods in Data Analysis: Theory, Applications and Algorithms |
| Basket: Speech, Vision and Language Technologies (At least 3 credits from the following) | |
|---|---|
| Course Code | Course Name |
| CS6370 | Information Retrieval |
| CS5700 | Text processing and Retrieval |
| CS6870 | Survaillance Video Analytics |
| CS6803 | Topics in Natural Language Processing |
| CS6140 | Video Content Analysis |
| HT5083 | Text-to-Speech Systems for Indian Languages |
| MA4143 | Introduction to Time Series Analysis |
| EE6307 | Speech Systems |
| EE6310 | Image and Video Processing |
| Basket: Other Applications of AI (At least 3 credits from the following) | |
|---|---|
| Course Code | Course Name |
| AI5153 | Mobile Robotics |
| BM5020 | Artificial Intelligence in Biomedicine and Healthcare |
| BT3203 | Machine learning for Bioinformatics |
| BM5033 | Statistical Inference Methods in Bioengineering |
| BM6140 | Theoretical and Computational Neuroscience |
| SM5010 | Autonomous Navigation |
| AI4503/AI45003 | Cyber AI |
| Minor in AI Curriculum | ||
|---|---|---|
| Course Code | Course Name | Credits |
| AI2000 (or) AI5000 (or) CS5590 (or) CS3390 (or) EE2802 | Foundations of Machine Learning | 3 |
| AI2100 (or) AI5100 | Deep Learning | 3 |
| List Below | Electives | 6 |
| Total | 12 | |
Students have to finish a total of 12 credits, with at least one course from each of the categories (rows) above. 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 elective credits to compensate.
Electives not in the given basket lists can be considered in a given basket with approval of faculty advisor (e.g. a new AI elective offered by a new faculty).
| Elective List for Minor in AI | |
|---|---|
| Course Code | Course Name |
| AI3102 | Sequence models |
| AI5040 | Game Theory and Mechanism Design |
| AI5120 | Explainability in ML |
| AI5153 | Mobile Robotics |
| CS5350 | Bayesian Data Analysis |
| CS5470 | Theory of Learning and Kernel Methods |
| CS5120 | Probability in Computing |
| CS6360 | Advanced topics in Maching Learning |
| CS6370 | Information Retrieval |
| CS5700 | Text processing and Retrieval |
| CS6870 | Survaillance Video Analytics |
| CS6803 | Topics in Natural Language Processing |
| CS6140 | Video Content Analysis |
| CS6890 | Fraud Analytics Using Predictive and Social Network Techniques |
| CS5600 | Data Mining |
| CS6460 | Visual Big Data Analytics |
| CS5320 | Distributed Computing |
| CS6713 | Scalable algorithms for data analysis |
| CS6070 | Tensor: Techniques, Algorithms and Applications |
| CS3563 | Introduction to DBMS II |
| BM5020 | Artificial Intelligence in Biomedicine and Healthcare |
| BT3203 | Machine learning for Bioinformatics |
| BM5033 | Statistical Inference Methods in Bioengineering |
| BM6140 | Theoretical and Computational Neuroscience |
| HT5083 | Text-to-Speech Systems for Indian Languages |
| MA4143 | Introduction to Time Series Analysis |
| MA4043 | Algebro-Geometric Methods in Data Analysis: Theory, Applications and Algorithms |
| EE5604 | Intro to Statistical Learning theory |
| EE5605 | Kernel Methods for ML |
| EE5470 | Nonlinear Control Techniques |
| EE5903 | Information Theory, Coding and Inference |
| EE5328 | Introduction to Submodular Functions |
| EE6307 | Speech Systems |
| EE6310 | Image and Video Processing |
| SM5010 | Autonomous Navigation |