School of Engineering and Technology
Master of Technology in Artificial Intelligence and Machine Learning
Programme Details
Course at a Glance
Credits
72
Semesters
4 Semesters
Specialisations
AI & ML
Seats
15
School
Engineering and Technology
Programme Structure
Semester I
5 theory courses, 3 laboratory courses, 1 seminar and 1 audit course. Credits 20
L denotes lecture hours per week, T tutorial hours per week and P practical hours per week. MSE denotes the mid semester examination, Att attendance, CA teacher continuous assessment, IA internal
assessment and ESE end semester examination.
| S. No. |
Code | Subject | Category | L | T | P | Cr | MSE 1+2 |
Att | CA | IA | ESE | Lab IA |
Lab ESE |
Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | MTBSC101 | Mathematical Foundations for Machine Learning | BSC | 3 | 1 | 0 | 4 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 2 | MTAIML101 | Advanced Machine Learning | PCC | 3 | 1 | 0 | 4 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 3 | MTAIML102 | Advanced Data Structures and Algorithms | PCC | 3 | 0 | 0 | 3 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 4 | MTPEC101 | Programme Elective I | PEC | 3 | 0 | 0 | 3 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 5 | MTHSM101 | Research Methodology and Intellectual Property Rights | HSMC | 2 | 0 | 0 | 2 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 6 | MTAIML151 | Advanced Machine Learning Lab | PCC | 0 | 0 | 2 | 1 | - | - | - | - | - | 50 | 50 | 100 |
| 7 | MTAIML152 | Advanced Data Structures and Algorithms Lab | PCC | 0 | 0 | 2 | 1 | - | - | - | - | - | 50 | 50 | 100 |
| 8 | MTAIML153 | Artificial Intelligence Tools and Platforms Lab | PCC | 0 | 0 | 2 | 1 | - | - | - | - | - | 50 | 50 | 100 |
| 9 | MTPROJ151 | Technical Seminar I | PROJ | 0 | 0 | 2 | 1 | - | - | - | - | - | 100 | - | 100 |
| 10 | MTAU101 | Audit Course I | AU | 2 | 0 | 0 | 0 | - | - | - | - | - | - | - | - |
| Semester total | 16 | 2 | 8 | 20 | 900 |
Semester II
5 theory courses, 3 laboratory courses and 1 audit course. Credits 20.
L denotes lecture hours per week, T tutorial hours per week and P practical hours per week. MSE denotes the mid semester examination, Att attendance, CA teacher continuous assessment, IA internal
assessment and ESE end semester examination.
| S. No. |
Code | Subject | Category | L | T | P | Cr | MSE 1+2 |
Att | CA | IA | ESE | Lab IA |
Lab ESE |
Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | MTAIML201 | Deep Learning and Neural Architectures | PCC | 3 | 1 | 0 | 4 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 2 | MTAIML202 | Generative AI and Large Language Models | PCC | 3 | 1 | 0 | 4 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 3 | MTAIML203 | Natural Language Processing and Speech Technologies | PCC | 3 | 0 | 0 | 3 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 4 | MTPEC201 | Programme Elective II | PEC | 3 | 0 | 0 | 3 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 5 | MTPEC202 | Programme Elective III | PEC | 3 | 0 | 0 | 3 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 6 | MTAIML251 | Deep Learning Lab | PCC | 0 | 0 | 2 | 1 | - | - | - | - | - | 50 | 50 | 100 |
| 7 | MTAIML252 | Generative AI Lab | PCC | 0 | 0 | 2 | 1 | - | - | - | - | - | 50 | 50 | 100 |
| 8 | MTAIML253 | Natural Language Processing Lab | PCC | 0 | 0 | 2 | 1 | - | - | - | - | - | 50 | 50 | 100 |
| 9 | MTAU201 | Audit Course II | AU | 2 | 0 | 0 | 0 | - | - | - | - | - | - | - | - |
| Semester total | 17 | 2 | 6 | 20 | 800 |
Semester III
2 theory courses and Dissertation Phase I. Credits 16.
L denotes lecture hours per week, T tutorial hours per week and P practical hours per week. MSE denotes the mid semester examination, Att attendance, CA teacher continuous assessment, IA internal
assessment and ESE end semester examination.
| S. No. |
Code | Subject | Category | L | T | P | Cr | MSE 1+2 |
Att | CA | IA | ESE | Lab IA |
Lab ESE |
Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | MTPEC301 | Programme Elective IV | PEC | 3 | 0 | 0 | 3 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 2 | MTOEC301 | Open Elective or approved MOOC course | OEC | 2 | 0 | 0 | 2 | 30 | 5 | 5 | 40 | 60 | - | - | 100 |
| 3 | MTPROJ351 | Dissertation Phase I | PROJ | 0 | 0 | 22 | 11 | - | - | - | - | - | 200 | 200 | 400 |
| Semester total | 5 | 0 | 22 | 16 | 600 |
Semester IV
Dissertation Phase II. Credits 16.
L denotes lecture hours per week, T tutorial hours per week and P practical hours per week. MSE denotes the mid semester examination, Att attendance, CA teacher continuous assessment, IA internal
assessment and ESE end semester examination.
| S. No. |
Code | Subject | Category | L | T | P | Cr | MSE 1+2 |
Att | CA | IA | ESE | Lab IA |
Lab ESE |
Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | MTPROJ451 | Dissertation Phase II | PROJ | 0 | 0 | 32 | 16 | - | - | - | - | - | 400 | 600 | 1000 |
| Semester total | 0 | 0 | 32 | 16 | 1000 |
Consolidated Credit Summary
| Semester | Courses listed | Credits | Cumulative credits |
|---|---|---|---|
| I | 10 | 20 | 20 |
| II | 9 | 20 | 40 |
| III | 3 | 16 | 56 |
| IV | 1 | 16 | 72 |
| Total | 72 | 72 |
| Component | Credits | Share | Comprising |
|---|---|---|---|
| Programme core theory | 18 | 25.0 per cent | Mathematical foundations, advanced machine learning, advanced data structures, deep learning, generative artificial intelligence and natural language processing |
| Programme electives | 12 | 16.7 per cent | Four electives, one in Semester I, two in Semester II and one in Semester III |
| Research methodology and open elective | 4 | 5.6 per cent | Research methodology and intellectual property rights, and one open elective or approved MOOC course |
| Laboratory | 6 | 8.3 per cent | Six laboratory courses across Semesters I and II |
| Seminar and dissertation | 32 | 44.4 per cent | Technical seminar, Dissertation Phase I and Dissertation Phase II |
Programme Elective, Open Elective and Audit Course Baskets
A candidate selects one course from each programme elective basket. An elective will be offered in a session
only where not fewer than eight candidates register for it, unless the Head of the Department directs
otherwise. The Board of Studies may revise a basket at a later meeting without altering the programme credit
structure.
Programme Elective I, offered in Semester I
| Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MTPEC101A | Optimization Techniques for Machine Learning | 3 | 0 | 0 | 3 |
| MTPEC101B | Advanced Database Systems and Data Engineering | 3 | 0 | 0 | 3 |
| MTPEC101C | Statistical Learning Theory | 3 | 0 | 0 | 3 |
| MTPEC101D | Advanced Computer Networks and Distributed Systems | 3 | 0 | 0 | 3 |
Programme Elective II, offered in Semester II
| Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MTPEC201A | Computer Vision and Video Analytics | 3 | 0 | 0 | 3 |
| MTPEC201B | Reinforcement Learning and Sequential Decision Making | 3 | 0 | 0 | 3 |
| MTPEC201C | Federated Learning and Privacy Preserving Computation | 3 | 0 | 0 | 3 |
| MTPEC201D | Graph Machine Learning | 3 | 0 | 0 | 3 |
Programme Elective III, offered in Semester II
| Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MTPEC202A | Machine Learning Operations and Scalable AI Systems | 3 | 0 | 0 | 3 |
| MTPEC202B | Explainable and Trustworthy Artificial Intelligence | 3 | 0 | 0 | 3 |
| MTPEC202C | Edge Artificial Intelligence and Embedded Intelligence | 3 | 0 | 0 | 3 |
| MTPEC202D | Time Series Analysis and Forecasting | 3 | 0 | 0 | 3 |
Programme Elective IV, offered in Semester III
| Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MTPEC301A | Artificial Intelligence in Healthcare | 3 | 0 | 0 | 3 |
| MTPEC301B | Artificial Intelligence for Agriculture and Earth Observation | 3 | 0 | 0 | 3 |
| MTPEC301C | Quantum Machine Learning | 3 | 0 | 0 | 3 |
| MTPEC301D | Multi Agent and Agentic Artificial Intelligence Systems | 3 | 0 | 0 | 3 |
Open Elective, offered in Semester III
| Code | Course Title | L | T | P | Credits |
|---|---|---|---|---|---|
| MTOEC301A | Research and Publication Ethics | 2 | 0 | 0 | 2 |
| MTOEC301B | Project Management for Technology Projects | 2 | 0 | 0 | 2 |
| MTOEC301C | Intellectual Property and Technology Transfer | 2 | 0 | 0 | 2 |
| MTOEC301D | Approved MOOC course of not less than twelve weeks | 2 | 0 | 0 | 2 |
Audit courses
A candidate takes one audit course in each of Semesters I and II. Audit courses carry no credit and are
recorded as satisfactory or unsatisfactory. Both must be cleared before the submission of the dissertation.
| Code | Course Title |
|---|---|
| MTAU01 | English for Research Paper Writing |
| MTAU02 | Disaster Management |
| MTAU03 | Value Education |
| MTAU04 | Constitution of India |
| MTAU05 | Pedagogy Studies |
| MTAU06 | Stress Management by Yoga |
| MTAU07 | Personality Development through Life Enlightenment Skills |