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.E. / B.Tech / M.Sc / MCA degree.
- 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.
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 Unit 7: Applications of AI & ML |
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 | 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 |
|---|---|
| 1 | Generative AI |
| 2 | Natural Language Processing |
| 3 | Deep Learning |
| 4 | Computer Vision |
| 5 | Graph Neural Networks |
| 6 | Agentic AI |
| 7 | Reinforcement Learning |
| 8 | Explainable AI (XAI) |
| 9 | Robotics and AI |
| 10 | AI for Financial Analytics |
| 11 | Deep Speech Processing |
| 12 | AI for Healthcare Data Analytics |
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
Program Certificate
Program Fee Details
Application Fee
Total Fee
Application Fee: ₹ 2,000
Program Fee (Inclusive of Application Fee) : ₹ 3,56,000
| Semester | Fee | Total |
|---|---|---|
| Sem 1 | 88500 | 88500 |
| Sem 2 | 88500 | 88500 |
| Sem 3 | 88500 | 88500 |
| Sem 4 | 88500 | 88500 |
| Total | 3,54,000 | 3,54,000 |
*Easy EMI Options are Available
*The application fees is strictly non-refundable and non-transferable.
Refund Policy
A refund is applicable after a deduction of 10000 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.
Program Director
Dr. Sunil Kumar P V
Assistant Professor
Indian Institute of Information Technology, Dharwad
Dr. Sunil Kumar P V is an accomplished academician and researcher in the field of Computer Science and Engineering, with a specialization in Machine Learning for Bioinformatics. He completed his PhD from NIT Calicut and has over 15 years of experience in teaching, research, and industry collaboration. Currently serving as an Assistant Professor at Indian Institute of Information Technology, Dharwad, Dr. Sunil has previously held faculty positions at CMR Institute of Technology, Vellore Institute of Technology, MES College of Engineering, and MEA Engineering College. He has published extensively in peer-reviewed journals and international conferences and has delivered numerous invited lectures and workshops on advanced computing, AI, and bioinformatics. With strong expertise in AI, ML, data analytics, and computational biology, he brings a rich blend of research excellence, teaching acumen, and industry engagement to lead and inspire the next generation of 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 Univeristy
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)
Prof. Rajesh Vasa
Adjunct Professor
PhD - Swinburne Univ. of Technology