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Program_Overview

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Extracted on 10 April 2026

Academic Structure

Overall Structure

There are six levels in the IIT Madras Degree program and to get the BS Degree in Data Science and Applications from IIT Madras, a learner has to successfully complete the first four levels.

There is also the flexibility to exit at any level. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education) or Diploma(s) from IIT Madras or BSc Degree in Programming and Data Science from IIT Madras or BS Degree in Data Science and Applications or PGD in Artificial Intelligence & Machine Learning or M.Tech in Artificial Intelligence & Machine Learning from IIT Madras.

Those who are interested in pursuing an exclusive Diploma Program in Programming or Data Science can also check out our Diploma Program website.

Courses and Credits in Each Level:

Foundation Level: 32 credits | 8 courses Diploma Level: - Programming: 27 credits | 6 courses + 2 projects - Data Science: 27 credits | 6 courses + 2 projects BSc Degree Level: 28 credits BS Degree Level: 28 credits PG Diploma Level: 20 credits | 3 core + 2 electives MTech Level: 20 credits | MTech Project

Total credits to be earned to get:

BSc Degree: 114 credits BS Degree: 142 credits PG Diploma in AI & ML: 162 credits (BS + PG Diploma) MTech in AI & ML: 182 credits (BS + PG Diploma + MTech)

Completion time: upto 8 years

The time period for this is based on learner’s preferred pace and performance in assessments. Expected learner engagement will be approximately 10hrs/course/week. Foundation Level: 1-3 years Diploma Level: 1-2 years each BSc/BS Degree Level: 1-2 years each PG Diploma: 1-2 years MTech: up to 8 years from starting of the Foundation level

Fees: Each term, pay only for courses you register for!

Refer Fee Structure.

Online Courses & Assignments

Duration of each course: 12 weeks - Each week comprising 2-3 hrs of videos, practice questions, text transcripts and online graded assignment(s).

Quizzes and Exams

In-person invigilated quizzes and exams as per the grading pattern defined for each course.

Term Structure

Every year is divided into three terms of four months each - January Term, May Term and September Term.

Each term of four months has 12 weeks of coursework (video lectures and assignments), 2 in-person invigilated Quizzes and End Term Exams. Depending on the course, assessments may include programming exams, mini projects, vivas, take home assignments, etc.

Fee Structure

For details about application fees, check Application Process in Admissions page.

Each term, pay only for the courses you register for in that specific term.

The fee structure has been revised for the students who will be joining the program at Foundation level from the January 2026 Term onwards.

Goal Total Credits Total Fees INR Foundation Only 32 ₹48,000 Foundation + One Diploma 59 ₹1,29,000 Foundation + Two Diplomas 86 ₹2,10,000 BSc Degree 114 ₹2,86,000 - ₹3,10,000 BS Degree 142 ₹3,86,000 - ₹4,50,000 PG Diploma in AI & ML 162 (BS + PG Diploma) ₹4,86,000 - ₹5,90,000 MTech in AI & ML 182 (BS + PG Diploma + MTech) ₹6,86,000 - ₹7,90,000

Existing Fee Structure for those students who joined the program till September 2025 term : Click here

The IITM BS program strives to secure scholarships from CSR and Alumni donations for its students from socially and economically disadvantaged backgrounds to cover the full program fees. Click here for details on industry CSR scholarships

As intermittent support till such donations are secured, IIT Madras covers part of the fees for the BS program students from socially and economically disadvantaged backgrounds.

The fraction of IIT Madras' fee support depends on the learner's category and family income and is given below:


Family Income > 5 LPA	Family Income > 1 LPA and <= 5 LPA	Family Income <= 1 LPA

Fee Support from IIT Madras Docs Required Fee Support from IIT Madras Docs Required Fee Support from IIT Madras Docs Required General N/A NIL 50% EWS + Family Income 75% EWS + Family Income OBC N/A NIL 50% OBC-NCL + Family Income 75% OBC-NCL + Family Income SC / ST 50% SC / ST 50% SC / ST 75% SC / ST + Family Income PwD 50% PwD 50% PwD 75% PwD + EWS / OBC-NCL + Family Income SC / ST + PwD 75% SC / ST + PwD 75% SC / ST + PwD 75% SC / ST + PwD

*Fee waiver is not applicable for the students outside India

Please note: At the Diploma and degree Levels of the program, a nominal fee may be charged towards additional verification of documents for those wishing to avail program fee waivers or CSR support.

The term family income for the purpose of availing IITM fee support includes the income of the candidate, the income of his/her parents and spouse, also the income of his/her siblings and children below the age of 18 years. Family income certificate is not required while applying for the Degree program, but will be required to avail IITM fee support when joining the program. Download Family Income Certificate format

OBC-NCL / EWS certificate, if applicable, need to be obtained in following format while applying: Download OBC-NCL Certificate format Download EWS Certificate format

Note: If a learner does not pass a course in the term they registered for, they will need to repeat the entire course in a later term with re-payment of full course fee. If a learner completed all course requirements, but couldn't attend the end term exam alone, they can choose to repeat just the end term exam in the next term with the payment of an end term exam fee (₹1000 for foundation level courses; ₹2000 for diploma / degree level courses).

Table 1

Goal	Total Credits	Total Fees INR
Foundation Only	32	₹48,000
Foundation + One Diploma	59	₹1,29,000
Foundation + Two Diplomas	86	₹2,10,000
BSc Degree	114	₹2,86,000 - ₹3,10,000
BS Degree	142	₹3,86,000 - ₹4,50,000
PG Diploma in AI & ML	162 (BS + PG Diploma)	₹4,86,000 - ₹5,90,000
MTech in AI & ML	182 (BS + PG Diploma + MTech)	₹6,86,000 - ₹7,90,000

Table 2

	Family Income > 5 LPA	Family Income > 1 LPA and <= 5 LPA	Family Income <= 1 LPA
Fee Support from IIT Madras	Docs Required	Fee Support from IIT Madras	Docs Required
General	N/A	NIL	50%
OBC	N/A	NIL	50%
SC / ST	50%	SC / ST	50%
PwD	50%	PwD	50%
SC / ST + PwD	75%	SC / ST + PwD	75%

Foundation Level

The Foundation Level comprises courses in Mathematics, Statistics, Basics of Programming and Python, and English. These courses have been chosen to ensure that the learner who passes these successfully is well prepared to proceed to the Diploma Level courses.

Requirements for registration

The learner should apply for and clear the Qualifier Process.

Options on successful completion

Learners have the following two options when they successfully complete all 8 Foundation Level courses:

Exit: The learner may exit with a Foundation Certificate from Centre for Outreach and Digital Education (CODE), IIT Madras. Proceed to next level: The learner can join the Diploma Level.

8 courses

32 credits

1 - 3 years

10 hrs/course/week

Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites

Mathematics for Data Science I 4 BSMA1001 None None

Statistics for Data Science I 4 BSMA1002 None None

Computational Thinking 4 BSCS1001 None None

English I 4 BSHS1001 None None

Mathematics for Data Science II 4 BSMA1003 BSMA1001 None

Statistics for Data Science II 4 BSMA1004 BSMA1002, BSMA1001 BSMA1003

Programming in Python 4 BSCS1002 BSCS1001 None

English II 4 BSHS1002 BSHS1001 None

Table 1

Course Name	Credits	Code	Prerequisites	Corequisites

Mathematics for Data Science I	4	BSMA1001	None	None
Statistics for Data Science I	4	BSMA1002	None	None
Computational Thinking	4	BSCS1001	None	None
English I	4	BSHS1001	None	None
Mathematics for Data Science II	4	BSMA1003	BSMA1001	None
Statistics for Data Science II	4	BSMA1004	BSMA1002, BSMA1001	BSMA1003
Programming in Python	4	BSCS1002	BSCS1001	None
English II	4	BSHS1002	BSHS1001	None

Diploma Level

There are two sections in the Diploma Level with courses for Diploma in Programming and courses for Diploma in Data Science. Each of these diplomas comprises 5 core courses, 2 projects and 1 skill enhancement course.

Requirements for registration

The learner should have cleared all 8 Foundation Level courses.

Options on successful completion

Learners have the following options based on the courses completed in this level:

If a learner has completed all the courses and projects in Foundation Level and both Diplomas, they can proceed to the BSc Degree Level. OR they may exit with a Diploma in Programming from IIT Madras. OR they may exit with a Diploma in Data Science from IIT Madras. OR they may exit with both Diplomas from IIT Madras.

12 courses + 4 projects

54 credits

1 - 3 years

15 hrs/course/week

Refer Fee Structure

Courses for Diploma in Programming

The Diploma in Programming lays a sturdy foundation in databases and programming concepts with data structures and algorithms. The learner goes on to apply these in the building of a web application by the end of the diploma.

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites Database Management Systems 4 BSCS2001 None None

Programming, Data Structures and Algorithms using Python 4 BSCS2002 None None

Modern Application Development I 4 BSCS2003 None BSCS2001

PROJECT Modern Application Development I - Project 2 BSCS2003P None BSCS2003

Programming Concepts using Java 4 BSCS2005 None None

Modern Application Development II 4 BSCS2006 BSCS2003 None

PROJECT Modern Application Development II - Project 2 BSCS2006P BSCS2003P BSCS2006

System Commands 3 BSSE2001 None None

Courses for Diploma in Data Science

The Diploma in Data Science exposes the learner to the holistic approach of gathering, analysing, and interpreting data for a variety of problems. The courses on Business Data lays down the context and the need for the data, while the Machine Learning courses equip the learner to use and analyse this data towards impactful conclusions.

Diploma in Data Science Pathway

Once the student is in the Diploma level, they will have 2 options to complete the Diploma in Data Science as shown below. Students can gain 27 Credits in Diploma in Data Science in 2 ways:

Mandatory: 21 Credits

5 Mandatory Courses + 1 Project

Choose Your Track for remaining 6 Credits

Choose one of two options (Option I or Option II)

Option 1: Business Analytics

Business Analytics + BDM Project

Option 2: Introduction to Deep Learning & AI

Introduction to Deep Learning and Generative AI + Project

Important Notes:

This is effective for students in the Diploma level from the September 2025 Term onwards. Students have to complete the mandatory 5 courses (19 credits) + 1 Project (2 Credits) The remaining 6 credits can be earned by choosing any one of the two options comprising a theory course and a project.

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites

Machine Learning Foundations 4 BSCS2004 None None

Business Data Management 4 BSMS2001 None None

Machine Learning Techniques 4 BSCS2007 None BSCS2004

Machine Learning Practice 4 BSCS2008 BSCS2004, BSCS2007 None

Machine Learning Practice - Project 2 BSCS2008P None BSCS2008

Tools in Data Science 3 BSSE2002 None BSCS2008

Business Data Management - Project OPTION 1 2 BSMS2001P None BSMS2001

Business Analytics OPTION 1 4 BSMS2002 BSMS2001 None

Introduction to Deep Learning and Generative AI OPTION 2 4 BSDA2001 None BSCS2008

Deep Learning and Generative AI - Project OPTION 2 2 BSDA2001P BSCS2007 BSCS2008, BSDA2001

Table 1

Course Name	Credits	Code	Prerequisites	Corequisites
Database Management Systems	4	BSCS2001	None	None
Programming, Data Structures and Algorithms using Python	4	BSCS2002	None	None
Modern Application Development I	4	BSCS2003	None	BSCS2001
PROJECT Modern Application Development I - Project	2	BSCS2003P	None	BSCS2003
Programming Concepts using Java	4	BSCS2005	None	None
Modern Application Development II	4	BSCS2006	BSCS2003	None
PROJECT Modern Application Development II - Project	2	BSCS2006P	BSCS2003P	BSCS2006
System Commands	3	BSSE2001	None	None

Table 2

Course Name	Credits	Code	Prerequisites	Corequisites

Machine Learning Foundations	4	BSCS2004	None	None
Business Data Management	4	BSMS2001	None	None
Machine Learning Techniques	4	BSCS2007	None	BSCS2004
Machine Learning Practice	4	BSCS2008	BSCS2004, BSCS2007	None
Machine Learning Practice - Project	2	BSCS2008P	None	BSCS2008
Tools in Data Science	3	BSSE2002	None	BSCS2008
Business Data Management - Project OPTION 1	2	BSMS2001P	None	BSMS2001
Business Analytics OPTION 1	4	BSMS2002	BSMS2001	None
Introduction to Deep Learning and Generative AI OPTION 2	4	BSDA2001	None	BSCS2008
Deep Learning and Generative AI - Project OPTION 2	2	BSDA2001P	BSCS2007	BSCS2008, BSDA2001

Diploma in Programming — Courses

Diploma Level

Requirements for registration

The learner should have cleared all 8 Foundation Level courses.

Options on successful completion

Learners have the following options based on the courses completed in this level:

12 courses + 4 projects

54 credits

1 - 3 years

15 hrs/course/week

Refer Fee Structure

Courses for Diploma in Programming

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites Database Management Systems 4 BSCS2001 None None

Programming, Data Structures and Algorithms using Python 4 BSCS2002 None None

Modern Application Development I 4 BSCS2003 None BSCS2001

PROJECT Modern Application Development I - Project 2 BSCS2003P None BSCS2003

Programming Concepts using Java 4 BSCS2005 None None

Modern Application Development II 4 BSCS2006 BSCS2003 None

PROJECT Modern Application Development II - Project 2 BSCS2006P BSCS2003P BSCS2006

System Commands 3 BSSE2001 None None

Courses for Diploma in Data Science

Diploma in Data Science Pathway

Once the student is in the Diploma level, they will have 2 options to complete the Diploma in Data Science as shown below. Students can gain 27 Credits in Diploma in Data Science in 2 ways:

Mandatory: 21 Credits

5 Mandatory Courses + 1 Project

Choose Your Track for remaining 6 Credits

Choose one of two options (Option I or Option II)

Option 1: Business Analytics

Business Analytics + BDM Project

Option 2: Introduction to Deep Learning & AI

Introduction to Deep Learning and Generative AI + Project

Important Notes:

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites

Machine Learning Foundations 4 BSCS2004 None None

Business Data Management 4 BSMS2001 None None

Machine Learning Techniques 4 BSCS2007 None BSCS2004

Machine Learning Practice 4 BSCS2008 BSCS2004, BSCS2007 None

Machine Learning Practice - Project 2 BSCS2008P None BSCS2008

Tools in Data Science 3 BSSE2002 None BSCS2008

Business Data Management - Project OPTION 1 2 BSMS2001P None BSMS2001

Business Analytics OPTION 1 4 BSMS2002 BSMS2001 None

Introduction to Deep Learning and Generative AI OPTION 2 4 BSDA2001 None BSCS2008

Deep Learning and Generative AI - Project OPTION 2 2 BSDA2001P BSCS2007 BSCS2008, BSDA2001

Table 1

Course Name	Credits	Code	Prerequisites	Corequisites
Database Management Systems	4	BSCS2001	None	None
Programming, Data Structures and Algorithms using Python	4	BSCS2002	None	None
Modern Application Development I	4	BSCS2003	None	BSCS2001
PROJECT Modern Application Development I - Project	2	BSCS2003P	None	BSCS2003
Programming Concepts using Java	4	BSCS2005	None	None
Modern Application Development II	4	BSCS2006	BSCS2003	None
PROJECT Modern Application Development II - Project	2	BSCS2006P	BSCS2003P	BSCS2006
System Commands	3	BSSE2001	None	None

Table 2

Course Name	Credits	Code	Prerequisites	Corequisites

Machine Learning Foundations	4	BSCS2004	None	None
Business Data Management	4	BSMS2001	None	None
Machine Learning Techniques	4	BSCS2007	None	BSCS2004
Machine Learning Practice	4	BSCS2008	BSCS2004, BSCS2007	None
Machine Learning Practice - Project	2	BSCS2008P	None	BSCS2008
Tools in Data Science	3	BSSE2002	None	BSCS2008
Business Data Management - Project OPTION 1	2	BSMS2001P	None	BSMS2001
Business Analytics OPTION 1	4	BSMS2002	BSMS2001	None
Introduction to Deep Learning and Generative AI OPTION 2	4	BSDA2001	None	BSCS2008
Deep Learning and Generative AI - Project OPTION 2	2	BSDA2001P	BSCS2007	BSCS2008, BSDA2001

Diploma in Data Science — Courses

Diploma Level

Requirements for registration

The learner should have cleared all 8 Foundation Level courses.

Options on successful completion

Learners have the following options based on the courses completed in this level:

12 courses + 4 projects

54 credits

1 - 3 years

15 hrs/course/week

Refer Fee Structure

Courses for Diploma in Programming

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites Database Management Systems 4 BSCS2001 None None

Programming, Data Structures and Algorithms using Python 4 BSCS2002 None None

Modern Application Development I 4 BSCS2003 None BSCS2001

PROJECT Modern Application Development I - Project 2 BSCS2003P None BSCS2003

Programming Concepts using Java 4 BSCS2005 None None

Modern Application Development II 4 BSCS2006 BSCS2003 None

PROJECT Modern Application Development II - Project 2 BSCS2006P BSCS2003P BSCS2006

System Commands 3 BSSE2001 None None

Courses for Diploma in Data Science

Diploma in Data Science Pathway

Once the student is in the Diploma level, they will have 2 options to complete the Diploma in Data Science as shown below. Students can gain 27 Credits in Diploma in Data Science in 2 ways:

Mandatory: 21 Credits

5 Mandatory Courses + 1 Project

Choose Your Track for remaining 6 Credits

Choose one of two options (Option I or Option II)

Option 1: Business Analytics

Business Analytics + BDM Project

Option 2: Introduction to Deep Learning & AI

Introduction to Deep Learning and Generative AI + Project

Important Notes:

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites

Machine Learning Foundations 4 BSCS2004 None None

Business Data Management 4 BSMS2001 None None

Machine Learning Techniques 4 BSCS2007 None BSCS2004

Machine Learning Practice 4 BSCS2008 BSCS2004, BSCS2007 None

Machine Learning Practice - Project 2 BSCS2008P None BSCS2008

Tools in Data Science 3 BSSE2002 None BSCS2008

Business Data Management - Project OPTION 1 2 BSMS2001P None BSMS2001

Business Analytics OPTION 1 4 BSMS2002 BSMS2001 None

Introduction to Deep Learning and Generative AI OPTION 2 4 BSDA2001 None BSCS2008

Deep Learning and Generative AI - Project OPTION 2 2 BSDA2001P BSCS2007 BSCS2008, BSDA2001

Table 1

Course Name	Credits	Code	Prerequisites	Corequisites
Database Management Systems	4	BSCS2001	None	None
Programming, Data Structures and Algorithms using Python	4	BSCS2002	None	None
Modern Application Development I	4	BSCS2003	None	BSCS2001
PROJECT Modern Application Development I - Project	2	BSCS2003P	None	BSCS2003
Programming Concepts using Java	4	BSCS2005	None	None
Modern Application Development II	4	BSCS2006	BSCS2003	None
PROJECT Modern Application Development II - Project	2	BSCS2006P	BSCS2003P	BSCS2006
System Commands	3	BSSE2001	None	None

Table 2

Course Name	Credits	Code	Prerequisites	Corequisites

Machine Learning Foundations	4	BSCS2004	None	None
Business Data Management	4	BSMS2001	None	None
Machine Learning Techniques	4	BSCS2007	None	BSCS2004
Machine Learning Practice	4	BSCS2008	BSCS2004, BSCS2007	None
Machine Learning Practice - Project	2	BSCS2008P	None	BSCS2008
Tools in Data Science	3	BSSE2002	None	BSCS2008
Business Data Management - Project OPTION 1	2	BSMS2001P	None	BSMS2001
Business Analytics OPTION 1	4	BSMS2002	BSMS2001	None
Introduction to Deep Learning and Generative AI OPTION 2	4	BSDA2001	None	BSCS2008
Deep Learning and Generative AI - Project OPTION 2	2	BSDA2001P	BSCS2007	BSCS2008, BSDA2001

BSc Degree Level

for BSc in Programming and Data Science

Requirements for registration

The learner should have cleared all 8 courses in Foundation Level and all 12 courses + 4 projects in Diploma Level.

Options on successful completion

Once the learner successfully completes overall 114 credits including credits earned in all previous levels:

they can proceed to the BS Degree Level. OR they may exit with a BSc Degree in Programming & Data Science from IIT Madras.

BSc Degree Level

28 credits (Total 114 credits)

1 - 3 years

15 hrs/course/week

Refer Fee Structure

BS Degree Level

for BS in Data Science and Applications

Requirements for registration

The learner should have earned 114 credits and completed the BSc Degree Level to enter the BS Degree Level.

Options on successful completion

Once the learner successfully completes 142 credits and the course requirements:

They can exit with a BS Degree in Data Science and Applications from IIT Madras. OR they can proceed to the PG Diploma Level (if they meet the eligibility criteria of minimum CGPA of 8.0).

BS Degree Level

28 credits (Total 142 credits)

1 - 3 years

15 hrs/course/week

Refer Fee Structure

Degree Level Courses

Core Courses

There are two pairs of core courses in the degree level. It is mandatory for the learner to complete all four core courses.

Core Courses Pair I Core Courses Pair II Software Engineering AI: Search Methods for Problem Solving Software Testing Deep Learning

Elective Courses

Here is the list of elective courses offered in the program. In the BSc and BS level, a maximum of 8 credits can be transferred from NPTEL and there is the option to do an apprenticeship and transfer up to a maximum of 12 credits in the BS level. (Note: List of elective courses may change each term depending on availability.)

Course Name Code Credits

Software Engineering CORE COURSE BSCS3001 4
Software Testing CORE COURSE BSCS3002 4
AI: Search Methods for Problem Solving CORE COURSE BSCS3003 4
Deep Learning CORE COURSE BSCS3004 4
Strategies for Professional Growth MANDATORY COURSE BSGN3001 4
Algorithmic Thinking in Bioinformatics BSBT4001 4
Big Data and Biological Networks BSBT4002 4
Data Visualization Design BSCS4001 4
Special topics in Machine Learning (Reinforcement Learning) BSDA5007 4
Speech Technology BSEE4001 4
Design Thinking for Data-Driven App Development BSMS4002 4
Industry 4.0 BSMS4001 4
Sequential Decision Making BSDA5007 4
Market Research BSMS3002 4
Privacy & Security in Online Social Media BSCS4003 4
Introduction to Big Data BSDA5001 4
Financial Forensics BSMS4003 4
Linear Statistical Models BSMA3012 4
Advanced Algorithms BSCS4021 4
Statistical Computing BSMA3014 4
Computer Systems Design BSCS3031 4
Programming in C BSCS3005 4
Mathematical Thinking BSMA2001 4
Large Language Models BSDA5004 4
Introduction to Natural Language Processing (i-NLP) BSDA5005 4
Deep Learning for Computer Vision BSDA5006 4
Managerial Economics BSMS3033 4
Game Theory and Strategy BSMS4023 4
Corporate Finance BSMS3034 4
Deep Learning Practice BSDA5013 4
Operating Systems BSCS4022 4
Mathematical Foundations of Generative AI BSDA5002 4
Algorithms for Data Science (ADS) BSDA5003 4
Machine Learning Operations (MLOps) BSDA5014 4
Data Science and AI Lab BSDA4001 4
App Dev Lab BSCS4010 4
Computer Networks BSCS4024 4
Theory of Computation BSCS3021 4

Table 1

Core Courses Pair I	Core Courses Pair II
Software Engineering	AI: Search Methods for Problem Solving
Software Testing	Deep Learning

Table 2

Course Name	Code	Credits
1. Software Engineering CORE COURSE	BSCS3001	4
2. Software Testing CORE COURSE	BSCS3002	4
3. AI: Search Methods for Problem Solving CORE COURSE	BSCS3003	4
4. Deep Learning CORE COURSE	BSCS3004	4
5. Strategies for Professional Growth MANDATORY COURSE	BSGN3001	4
6. Algorithmic Thinking in Bioinformatics	BSBT4001	4
7. Big Data and Biological Networks	BSBT4002	4
8. Data Visualization Design	BSCS4001	4
9. Special topics in Machine Learning (Reinforcement Learning)	BSDA5007	4
10. Speech Technology	BSEE4001	4
11. Design Thinking for Data-Driven App Development	BSMS4002	4
12. Industry 4.0	BSMS4001	4
13. Sequential Decision Making	BSDA5007	4
14. Market Research	BSMS3002	4
15. Privacy & Security in Online Social Media	BSCS4003	4
16. Introduction to Big Data	BSDA5001	4
17. Financial Forensics	BSMS4003	4
18. Linear Statistical Models	BSMA3012	4
19. Advanced Algorithms	BSCS4021	4
20. Statistical Computing	BSMA3014	4
21. Computer Systems Design	BSCS3031	4
22. Programming in C	BSCS3005	4
23. Mathematical Thinking	BSMA2001	4
24. Large Language Models	BSDA5004	4
25. Introduction to Natural Language Processing (i-NLP)	BSDA5005	4
26. Deep Learning for Computer Vision	BSDA5006	4
27. Managerial Economics	BSMS3033	4
28. Game Theory and Strategy	BSMS4023	4
29. Corporate Finance	BSMS3034	4
30. Deep Learning Practice	BSDA5013	4
31. Operating Systems	BSCS4022	4
32. Mathematical Foundations of Generative AI	BSDA5002	4
33. Algorithms for Data Science (ADS)	BSDA5003	4
34. Machine Learning Operations (MLOps)	BSDA5014	4
35. Data Science and AI Lab	BSDA4001	4
36. App Dev Lab	BSCS4010	4
37. Computer Networks	BSCS4024	4
38. Theory of Computation	BSCS3021	4

PG Diploma / MTech / Certificates

PG Diploma Level

for PG Diploma in AI & ML from IIT Madras

Requirements for registration

Students must have completed all core course requirements at the Degree level and must complete the CGPA requirements at the time of applying for the upgrade.

Options on successful completion

Once the learner successfully completes the course requirements, they can either continue to MTech Level for additional 20 credits (MTech Project) and complete MTech degree or exit with a PG Diploma in AI & ML from IIT Madras.

PG Diploma in AI & ML

20 credits (3 core + 2 electives)

1 - 2 years

15 hrs/course/week

Refer Fee Structure

PG Diploma Level Courses

Core Courses

There are three core courses in the PG Diploma level. It is mandatory for the learner to complete all three core courses.

Course Name Code Credits ML Ops BSDA5014 4 Generative AI BSDA5002 4 Algorithms for Data Science BSDA5003 4

Elective Courses

Here is the list of elective courses offered in the PG Diploma Programme. Student can choose 2 elective courses from the following options for a total of 8 credits. (Note: List of elective courses may change each term depending on availability.)

Course Name Code Credits

Large Language Models BSDA5004 4
Introduction to Natural Language Processing (i-NLP) BSDA5005 4
Deep Learning for Computer Vision BSDA5006 4
Reinforcement Learning BSDA5007 4
Responsible AI BSDA6001 4
Statistical Learning Theory BSDA6002 4
Deploybility Aspects of AI BSDA6003 4
Sequential Decision Making BSDA6004 4
Information Theory and Learning BSDA6005 4
Speech Technology BSEE5001 4
Research Project BSDA6006 4

Table 1

Course Name	Code	Credits
ML Ops	BSDA5014	4
Generative AI	BSDA5002	4
Algorithms for Data Science	BSDA5003	4

Table 2

Course Name	Code	Credits
1. Large Language Models	BSDA5004	4
2. Introduction to Natural Language Processing (i-NLP)	BSDA5005	4
3. Deep Learning for Computer Vision	BSDA5006	4
4. Reinforcement Learning	BSDA5007	4
5. Responsible AI	BSDA6001	4
6. Statistical Learning Theory	BSDA6002	4
7. Deploybility Aspects of AI	BSDA6003	4
8. Sequential Decision Making	BSDA6004	4
9. Information Theory and Learning	BSDA6005	4
10. Speech Technology	BSEE5001	4
11. Research Project	BSDA6006	4

PG Diploma / MTech / Certificates

MTech Level

for MTech in AI & ML from IIT Madras

Requirements for registration

Must have completed PG Diploma Level (20 credits) to be eligible for MTech registration. Students can start the project after completing the PG Diploma. Projects can be done in a company or research lab.

Exit

Once the learner successfully completes the MTech requirements:

Students who complete the mandatory MTech Project in AI & ML earn a BS + MTech degree from IIT Madras. The project is executed and evaluated in the same way as Web MTech programs with mandatory project work in broad areas of Machine Learning and AI. Project Timeline: M.Tech should be completed within 8 years from the starting of the program.

MTech in AI & ML

20 credits (MTech Project)

Flexible Timeline

Over approximately 5 years

Refer Fee Structure

MTech Level Courses

Core Courses

There are three core courses in the MTech level. It is mandatory for the learner to complete all three core courses.

Course Name Course Code Credits MTech Project BSDA6901 20

Table 1

Course Name	Course Code	Credits
MTech Project	BSDA6901	20

PG Diploma / MTech / Certificates

Sample Certificates

MTech in AI & ML from IIT Madras

PG Diploma in AI & ML from IIT Madras

BS in Data Science and Applications from IIT Madras

BSc in Programming and Data Science from IIT Madras

Diploma in Programming from IIT Madras

Diploma in Data Science from IIT Madras

Advanced Certificate in Programming and Application Development from IIT Madras

Advanced Certificate in Machine Learning and Data Science from IIT Madras

Foundation Certificate from CODE, IIT Madras

Assessments

There are 3 types of assessments for each course: Weekly Assignments which are online monthly in-person Quizzes in-person End Term Exam View More Details

In addition, assessments may include programming exams, mini projects, vivas, take home assignments, etc.

Quiz & Exam Details

Assessments

There are 3 types of assessments for each course: Weekly Assignments which are online monthly in-person Quizzes in-person End Term Exam View More Details

In addition, assessments may include programming exams, mini projects, vivas, take home assignments, etc.

Course Registration

Course Registrations

In each term, a learner may register for upto 4 courses depending on their CCC (Credit Clearing Capability).

A learner’s CCC in the Foundation Level is calculated based on their performance in the Qualifier Exam or the previous term’s End Term Exams. The CCC in the Diploma Level and thereafter is 4.

Level Progression Requirements: • Foundation Level: All 8 courses must be successfully completed before enrolling in any Diploma Level course. • Diploma Level: All courses and projects must be successfully completed before enrolling in any Degree Level course. • BS Degree Level: All courses must be successfully completed before enrolling in PG Diploma Level. • PG Diploma Level: All courses must be successfully completed before enrolling in MTech Level.

Exam Cities

The Invigilated Quizzes and End Term exams are conducted in a number of cities spread across India. The map shows our current Exam Cities List. View List

Students residing/physically present in India on exam day

All students residing in India or physically present in India on the day of an in-centre exam must write exams at one of the exam centres in india.

Learners based outside India

We also conduct in-person exams in Bahrain, Kuwait, Oman and UAE.

Learners based out of other countries will be allowed to take up remote proctored exams. On exam day, students writing such internet based exams will be asked to pin the location exam is being taken from.

If any overseas students are planning to be in India on exam day, it is the student's responsibility to notify us ahead of time so that we can arrange for you to write the exam(s) in one of the exam centres in india; hall tickets will also be issued suitably. If any of these norms are violated, it will be considered as malpractice. Exam results may be withheld pending investigation and findings of the exam committee.

Note: Additional Exam Fee applies for all learners opting to write exams outside India.

If you reside outside India and cannot find a centre in your city / country, please write to ge@study.iitm.ac.in for assistance.