M.Tech in Computer Science & Engineering (Specialization: Artificial Intelligence & Machine Learning)
The M.Tech in Computer Science & Engineering with a specialization in Artificial Intelligence and Machine Learning is a two-year online postgraduate program, featuring on-campus immersion sessions, designed for professionals and students aiming to develop advanced expertise in AI, intelligent algorithms, and data-driven solutions. The program combines research-focused learning, practical projects, and industry exposure to equip participants for impactful careers in technology leadership, product development, and research. Graduates will gain the skills to design and implement intelligent systems capable of learning, adapting, and evolving across diverse industries.
Eligibility Criteria:
- Educational Qualification :
- B.Tech / B.E. in circuit branches only (CSE, AIML, DSAI, Cybersecurity, IT, Information Science, ECE, EEE, Instrumentation and related streams)
- M.Sc. in CSE / IT / ECE
- MCA
- Employment Status : Candidate should be currently employed.
- Academic Performance:
- General / OBC category
- Minimum CGPA/CPI: 6.5 on a 10-point scale, or Minimum aggregate percentage: 60%.
- SC / ST / PwD category
- Minimum CGPA/CPI: 6.0 on a 10-point scale, or Minimum aggregate percentage: 55%.
- Check Eligibility
Program Objectives
Master AI & ML Technologies
Gain comprehensive knowledge of Artificial Intelligence, Machine Learning, Deep Learning, and emerging technologies to solve complex, real-world problems.
Research-Driven Problem Solving
Develop the ability to plan, execute, and assess research projects, contributing to innovations in academia and industry.
Data-Informed Decision Making
Strengthen skills in data analytics, predictive modeling, and AI-driven decision frameworks for effective problem resolution.
Practical Technical Competence
Acquire hands-on expertise through projects, simulations, and lab exercises, applying AI & ML methods to areas like computer vision, robotics, and autonomous systems.
Ethical Innovation
Foster responsible AI practices, ethical technology use, and creative thinking for meaningful societal and organizational impact.
Interdisciplinary Collaboration
Prepare to work in multi-disciplinary teams, integrating AI & ML solutions across diverse domains.
Career-Ready Skills
Equip graduates for leadership roles in R&D, AI/ML development, data science, and advanced research programs, ensuring readiness for technology-driven careers.
Learning Outcomes for AI & ML Professionals
Develop AI & ML Solutions: Design and implement machine learning algorithms, deep learning models, and AI systems to address practical challenges.
Lead Research and Innovation Projects: Conduct research surveys, experimental studies, and capstone projects demonstrating analytical rigor and creativity.
Apply AI to Real-World Scenarios: Utilize AI & ML techniques in computer vision, robotics, autonomous systems, and other applied domains.
Analyze Complex Data: Interpret large-scale datasets using mathematical, statistical, and computational approaches to inform decision-making.
Implement Responsible AI: Apply ethical, explainable AI (XAI), and secure AI practices in research and industry projects.
Collaborate Across Domains: Work effectively in interdisciplinary teams, applying AI & ML knowledge to engineering, business, and societal challenges.
Demonstrate Impact: Showcase semester and capstone projects that translate learning into measurable technical and strategic outcomes.
Program Highlights
Prestigious M.Tech Degree
Earn a highly respected M.Tech in Computer Science & Engineering with specialization in AI and ML from an Institute of National Importance.
Advanced, Industry-Relevant Curriculum
Master AI topics like Deep Learning, Reinforcement Learning, Explainable AI, and Computer Vision alongside strong engineering foundations.
Hands-On Campus Immersion
Engage in a 7-day campus immersion for hands-on sessions, faculty interactions, and networking with peers and experts.
Expert Faculty Guidance
Learn from distinguished IIIT Dharwad professors and industry leaders who bring a perfect mix of academic rigor and real-world insights.
Capstone Project
Work on a research-driven, practical project solving real challenges — demonstrate your strategic and technical expertise.
Professional Networking
Collaborate with a diverse cohort of professionals, researchers, and practitioners to build lifelong professional connections.
Strong Industry Connect
Benefit from collaborations with top tech companies, gaining exposure to cutting-edge tools and practices.
Flexible Online Learning
Learn at your own pace with live interactive sessions and immersive campus components tailored for professionals.
IIIT Dharwad Alumni Status
Join an elite alumni network of AI & ML experts and leaders from IIIT Dharwad’s executive programs.
Career Readiness
This program also offers a career readiness workshop focused on resume building and LinkedIn profile optimization to enhance professional visibility and job readiness.
Course Structure
| Course Type | Course Name | Credits |
|---|---|---|
| DisCore |
Applied Mathematics for Computer Science Unit 1: Linear Algebra Unit 2: Optimization Unit 3: Probability and Stochastic Process |
3 |
| DisCore |
Advanced Data Structures and Algorithms Unit 1: Growth Functions Unit 2: Trees Unit 3: Graph Algorithms Unit 4: Algorithm Design Strategies Unit 5: Complexity Classes |
3 |
| DisCore |
Programming Paradigms Lab Unit 1: Procedural Programming Unit 2: Object-Oriented Programming (OOP) Unit 3: Functional Programming Unit 4: Concurrent & Parallel Execution Unit 5: Declarative and Logic Programming Unit 6: Scripting & Automation |
2 |
| Elective |
Introduction to AI/ML Unit 1: Introduction to AI Unit 2: Problem Solving using Search Unit 3: Knowledge Representation Unit 4: Introduction to Machine Learning Unit 5: Supervised Learning Unit 6: Unsupervised Learning |
1 |
| Elective |
Introduction to Cybersecurity Unit 1: Introduction to Cybersecurity Unit 2: Identity & Access Management Unit 3: Standards & Regulations |
1 |
| Elective |
Introduction to Cloud Computing Unit 1: Introduction to Cloud Computing Unit 2: Cloud Service Models and Deployment Models |
1 |
| Master’s Core |
Introduction to Research Unit 1: Introduction Unit 2: Literature Review Unit 3: Research Exploration Unit 4: Patenting and Publications Unit 5: Presentation, Report and Thesis Writing Unit 6: Conclusions and Future Scope Unit 7: Principles & Ethics in Research |
2 |
| Project | Project-I | 3 |
| Course Type | Course Name | Credits |
|---|---|---|
| DisCore |
Advanced Computing Lab Unit 1: Advanced Programming and Performance Engineering Unit 2: Parallel Computing Platforms Unit 3: Distributed Computing Frameworks Unit 4: GPU and Accelerated Computing Unit 5: Cloud and Container-based Computing Unit 6: AI/ML Pipeline Engineering |
2 |
| Master’s Core | Literature Review and Seminar | 2 |
| Elective | Electives (1/2/3/4 credits) | 5 |
| Project | Project-II | 6 |
| Course Type | Course Name | Credits |
|---|---|---|
| Project | Project-III | 9 |
| Elective | Electives (1/2/3/4 credits) | 6 |
| Course Type | Course Name | Credits |
|---|---|---|
| Project | Project-IV | 12 |
| Elective | Electives (1/2/3 credits) | 3 |
| Sl. no | Course name | Credits |
|---|---|---|
| 1 |
Generative AI Overview of AI and Generative AI Tools and Models in Generative AI Applications of Generative AI |
2 |
| 2 |
Speech and Natural Language Processing Lexical Processing in NLP Syntactic Processing in NLP Semantic Processing in NLP Phonetics & Speech Processing Automation Speech Recogniton & Text-to-Speech |
2 |
| 3 |
Deep Learning Review of Neural Networks Deep Feedforward Neural Networks (DNNs) Convolutional Neural Networks (CNNs) Sequence Models Generative Models |
3 |
| 4 |
Computer Vision Foundations of Neural Networks Convolutional Neural Networks (CNN) for Image Classification Evaluating Datasets using Metrics CNNs for Segmentation Recurrent Neural Networks for Vision |
2 |
| 5 |
Graph Neural Networks Graph Fundamentals Graph Neural Networks Foundations Core Architectures Advanced Topics Applications |
1 |
| 6 |
Agentic AI Foundations of Intelligent Agents Planning & Decision Making Learning-Based Agents LLM-based and Tool using agents Safety, Ethics and Evaluation |
2 |
| 7 |
Reinforcement Learning Introduction Classical Reinforcement Learning Methods Deep Learning Based Reinforcement Learning |
2 |
| 8 |
Explainable AI (XAI) Foundations Interpretable /Inherently Interpretable Models Model-Agnostic Post-Hoc Methods Visual XAI Evaluation Ethics |
1 |
| 9 |
Robotics and AI Robotics Fundamentals Perception for Robotics Planning and Control Learning in Robotics Application and Case Studies |
2 |
| 10 |
AI for Financial Analytics Financial Data & Markets Time Series Modeling Risk & Fraud Analytics Algorithmic Trading (Conceptual) Ethics and Regulation |
2 |
| 11 |
Deep Speech Processing Overview of Speech Processing Machine Learning and Speech Processing Deep Learning and Speech Processing |
2 |
| 12 |
AI for Healthcare and Data Analytics Historical evolution of AI in Medicine Clinical Decision Support Systems Case Study |
2 |
Alumni Privileges
Students will receive an official Institute Email ID and ID card, and will be eligible to participate in institute events and activities. Upon completion, they become part of the institute’s alumni network.
Tools You'll Master
*Tool exposure varies by specialization; students may not work with all tools listed.
Assessment & Evaluation
Students will be evaluated through a combination of quizzes, assignments, case studies, and end-term examinations. These diverse assessment methods ensure continuous learning and a well-rounded understanding of the subject.
Pedagogy & Delivery
The program follows a blended learning approach, combining multiple instructional methods to enhance learning outcomes:Engage with faculty and peers through discussions, Q&A, and case study analysis.
Access recorded lectures, readings, and practice exercises to reinforce concepts.
Apply knowledge through projects, simulations, and hands-on assignments.
Participate in group exercises, peer learning, and forums to foster teamwork and practical understanding.
Admission Process
Note:
- Admissions will be offered based on merit, as evaluated through the application and personal interview process.
- Offers of admission will also follow a first-come, first-served basis, subject to meeting the eligibility and selection criteria and availability of seats.
Program Certificate
Program Fee Details
Application Fee
Total Fee
| Sem 1 | Sem 2 | Sem 3 | Sem 4 |
|---|---|---|---|
| ₹ 88,500 | ₹ 88,500 | ₹ 88,500 | ₹ 88,500 |
*Easy EMI Options are Available
*The application fee is strictly non-refundable and non-transferable.
Refund Policy
A refund is
applicable after a deduction of ₹ 10,000 before commencement of the batch, provided the
course
material has not been accessed or downloaded. No refund will be provided on or after the
batch
commencement date.
Application Fee: ₹ 2,000
Program Fee (Inclusive of Application Fee) : ₹ 3,56,000
Note: The institute is not responsible for EMI-related arrangements.
Campus Immersion
Experience structured on-campus immersions after every semester, designed to strengthen academic engagement, peer collaboration, and faculty interaction. These campus immersions complement the live online learning format by connecting structured online learning with in-person academic engagement within a nationally recognized institute of technology.
What’s Included
-
Academic EngagementFaculty-led sessions, discussions, and academic interactions aligned with the programme curriculum.
-
Peer NetworkingCollaborative learning and networking with fellow participants.
-
Campus ExperienceOpportunity to explore the IIIT Dharwad campus, engage with its academic ecosystem, and gain exposure to labs and research facilities.
What’s Not Included
-
TravelTravel to and from the campus.
-
Accommodation & MealsFood, lodging, and stay arrangements.
-
Personal ExpensesAny personal expenses incurred during the immersion period.
Not Sure If This Is the Right Fit?
Have a quick conversation to understand the academic depth, outcomes, and what this programme really offers. Our experts are here to help you make an informed decision.
Program Coordinator
Dr. Krishnendu Ghosh
Assistant Professor
Indian Institute of Information Technology, Dharwad
Dr. Krishnendu Ghosh is an academician and researcher in the field of Computer Science and Engineering, with a specialization in Speech Processing, Natural Language Processing, and Artificial Intelligence. He earned his M.S. (2012) and Ph.D. (2021) from IIT Kharagpur, where his master’s research focused on text analysis modules for text-to-speech systems, and his doctoral work addressed multimodal and AI-supported enrichment of learning materials. With over 11 years of research experience and 6+ years of teaching experience, Dr. Ghosh is currently serving as an Assistant Professor at the Indian Institute of Information Technology (IIIT) Dharwad. His research interests include speech processing, natural language processing, artificial intelligence, information retrieval, and educational technology, where he has contributed extensively to peer-reviewed journals, international conferences, and funded research & academic projects. Passionate about bridging research, teaching, and real-world impact, Dr. Ghosh mentors undergraduate and postgraduate students in AI/ML and SNLP projects, fostering innovation, critical thinking, and applied research skills among future technology professionals.
Program Faculty
Dr. Abdul Wahid
Assistant Professor
Ph.D. (IIT Dhanbad)
Dr. Animesh Roy
Assistant Professor
Ph.D. (IIEST)
Dr. Dibyajyoti Guha
Assistant Professor
Ph.D. (IIT Bhubaneswar)
Dr. Girish G N
Assistant Professor
Ph.D. (NITK)
Dr. Krishnendu Ghosh
Assistant Professor
Ph.D. (IIT Kharagpur)
Dr. Malay Kumar
Assistant Professor
Ph.D. (NIT Raipur)
Dr. Milind Chabbi
Professor of Practice
Rice University
Dr. Pavan Kumar C
Assistant Professor
Ph.D. (VIT Vellore)
Dr. Prabhu Prasad B M
Assistant Professor
Ph.D. (IIT Bhubaneswar)
Dr. Pramod Yelmewad
Assistant Professor
Ph.D. (NITK Surathkal)
Dr. Shrinivas Kulkarni
Professor of Practice
PhD - University of Edinburgh
Dr. Sunil C K
Assistant Professor
Ph.D. (NITK Surathkal)
Dr. Sunil Kumar P V
Assistant Professor
Ph.D. (NIT, Calicut)
Dr. Suvadip Hazra
Assistant Professor
Ph.D. (NIT Durgapur)
Dr. Vivekraj V K
Assistant Professor
Ph.D. (IIT Roorkee)
Dr. Animesh Chaturvedi
Assistant Professor
Ph.D. (IIT Indore)
Dr. Chinmayananda A
Assistant Professor
Ph.D. (IISc Bengaluru)
Dr. Deepak K T
Assistant Professor
Ph.D. (IIT Guwahati)
Dr. Girish Revadigar
Assistant Professor
PhD (The University of New South Wales (UNSW) Sydney, Australia)
Dr. Manjunath K V
Assistant Professor
Ph.D. (NITK Surathkal)
Dr. Nataraj K S
Assistant Professor
Ph.D. (IIT Bombay)
Dr. Rajendra Hegadi
Associate Professor
Ph.D. (MGRRI, Chennai)
Dr. Ramesh Athe
Assistant Professor
Ph.D. (Osmania University, Hyderabad)
Dr. Shirshendu Layek
Assistant Professor
Ph.D. (IIT Dhanbad)
Dr. Shruti Maralappanavar
Assistant Professor
PhD: IIT Dharwad
Dr. Sunil Saumya
Assistant Professor
Ph.D. (NIT Patna)
Dr. Swagatika Sahoo
Assistant Professor
PhD: IIT Patna
Dr. Siddharth R
Assistant Professor
Ph.D. (NIT Puducherry)
Dr. Utkarsh Khaire
Assistant Professor
Ph.D. (NIT Nagaland)
Prof. S R Mahadeva Prasanna
Professor and Director
Ph.D. (IIT Madras)
