Program Objectives
Advanced Computing Foundations
Develop a strong understanding of core and advanced Computer Science & Engineering concepts essential for modern computing systems.
System Design & Implementation
Enable learners to analyze, design, and implement scalable software and system-level solutions for real-world challenges.
Research & Analytical Skills
Cultivate research aptitude through structured inquiry, literature review, experimentation, and project-based learning.
Domain Specialization
Enable learners to build focused expertise in a chosen computing domain through elective coursework and applied project work aligned with career goals.
Cross-Domain Computing
Equip learners to apply advanced computing techniques across diverse technical and industry contexts.
Professional & Ethical Practice
Foster responsible, ethical, and professional practices aligned with contemporary engineering standards.
Advanced Career Readiness
Prepare graduates for advanced technical positions, system design responsibilities, and research-oriented career pathways.
Industry-Relevant Skills
Strengthen practical, job-relevant skills by applying advanced computing concepts to real-world industry and research problems.
Learning Outcomes
Design and implement advanced software and computing solutions using sound engineering methodologies.
Apply core computer science and advanced computing techniques to solve complex technical problems.
Conduct research-driven analysis and communicate findings effectively.
Utilize modern computing platforms, development tools, and system architectures.
Translate academic learning into practical outcomes through semester projects and the capstone.
Collaborate effectively in multidisciplinary and professional engineering environments.
Develop focused expertise in a chosen area of interest through elective coursework and applied project work, enabling deeper alignment with career goals and industry trends.
Program Highlights
Prestigious M.Tech Degree
Earn an M.Tech in Computer Science & Engineering from IIIT Dharwad, an Institute of National Importance.
Elective Domain Specialisation
Pursue focused expertise in a computing domain of your choice through a flexible elective structure aligned with career aspirations.
Advanced Computing Curriculum
Study a rigorous curriculum covering core computer science foundations, advanced computing concepts, and applied engineering practices.
Research-Oriented Learning
Build strong research and analytical skills through structured research modules, literature review, and project-based learning.
Hands-On Capstone Project
Apply advanced computing knowledge to solve real-world engineering problems through a research-driven capstone project.
Campus Immersion Experience
Participate in a seven-day on-campus immersion for academic engagement, peer learning, and faculty interaction.
Distinguished Faculty
Learn from experienced IIIT Dharwad faculty and industry practitioners with strong academic and applied expertise.
IIIT Dharwad Alumni Status
Graduate with official IIIT Dharwad alumni status and lifelong academic recognition.
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 |
Specialization Offered
Artificial Intelligence & Machine Learning
- Generative AI
- Natural Language Processing
- Deep Learning
- Computer Vision
- Graph Neural Networks
- Agentic AI
- Reinforcement Learning
- Explainable AI (XAI)
- Robotics and AI
- AI for Financial Analytics
- Deep Speech Processing
- AI for Healthcare Data Analytics
Cyber Security
- Computer System Security
- Security in Cloud Computing
- AI for Cybersecurity
- Biometric Security and Forensics
- Forensic Data Recovery
- Deep Learning
- Dev sec ops
Cloud Computing
- Distributed and parallel systems
- Security in Cloud Computing
- Site reliability engineering in cloud computing
- Biometric Security and Forensics
- Big data systems
- Edge AI
- High Performance Computer Architecture
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