Modern Computer Vision Course | IIT Madras | Deep Learning & AI
Course Details
| Exam Registration | 208 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Electrical, Electronics and Communications Engineering, Communication and Signal Processing |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 17 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master the Future of AI with a Modern Computer Vision Course
In today's technology-driven world, the ability for machines to see and interpret visual information is revolutionizing industries from healthcare to autonomous driving. Computer Vision (CV) sits at the heart of this transformation, blending image processing, machine learning, and geometric understanding. If you're looking to build a strong foundation in this cutting-edge field, the Modern Computer Vision course offered by the prestigious Indian Institute of Technology (IIT) Madras is an unparalleled opportunity.
Course Overview: Bridging Classical and Modern Techniques
This meticulously designed 12-week program provides a holistic journey through the world of computer vision. It uniquely balances classical, algorithm-based approaches with state-of-the-art deep learning methodologies. The course is structured to take learners from the fundamental concepts of neural networks to advanced topics like 3D scene reconstruction and object detection, ensuring a comprehensive understanding of the entire vision pipeline.
Learn from an Esteemed Expert: Prof. A.N. Rajagopalan
The course is led by Prof. A.N. Rajagopalan, a distinguished Professor of Electrical Engineering at IIT Madras. A recognized authority in Image Processing and Computer Vision, Prof. Rajagopalan is a Fellow of national and international academies and serves on the editorial boards of flagship IEEE journals in the field. His expertise, backed by co-authorship of two books, ensures the curriculum is both academically rigorous and aligned with real-world applications.
Who Should Enroll?
This course is ideally suited for:
- Undergraduate and Postgraduate students in Electrical, Electronics, Communications Engineering, and Computer Science.
- Professionals in Signal Processing, Robotics, and AI/ML seeking to specialize in vision.
- Researchers and developers aiming to solidify their theoretical understanding and practical skills in CV.
Prerequisites: While familiarity with image processing, linear algebra, and probability is beneficial, the course is designed to be accessible, making it an excellent starting point for dedicated learners.
Detailed 12-Week Course Layout
The curriculum is logically sequenced to build knowledge progressively:
| Week | Core Topics |
|---|---|
| Weeks 1-4 | Deep Learning Foundations: Introduction to neurons, MLPs, backpropagation, CNNs, RNNs, optimization, and regularization. |
| Weeks 5-7 | Low-Level & Feature-Based Vision: Spatial/frequency filtering, edge/line detection, feature detectors (Harris, SIFT, SURF). |
| Weeks 8-10 | 3D Geometry & Reconstruction: Camera models, epipolar geometry, stereo vision, Structure from Motion (SFM). |
| Weeks 11-12 | Mid & High-Level Vision: Image segmentation, object detection, and deep learning applications for these tasks. |
Industry Relevance and Career Prospects
The skills acquired in this course are in high demand across the global tech landscape. The curriculum is directly supported by and relevant to industry leaders including Google, Amazon (AWS), Meta (Facebook), Qualcomm, Texas Instruments, KLA-Tencor, Siemens, GE, and Philips. Mastery of these topics opens doors to roles in:
- Autonomous Systems and Self-Driving Cars
- Medical Image Analysis and Diagnostics
- Augmented and Virtual Reality (AR/VR)
- Industrial Automation and Quality Inspection
- Computational Photography and Smartphone Imaging
Key Learning Resources
Participants will engage with a seminal textbook in the field: "Computer Vision: Algorithms and Applications" by R. Szeliski (Springer, 2010). This resource, available as an online draft, provides an excellent complement to the lecture materials, offering in-depth explanations and further reading.
Why Choose This Computer Vision Course?
This course stands out by offering a complete, integrated view of computer vision. Unlike programs that focus solely on deep learning, this curriculum ensures you understand the classical problems and solutions, giving you the insight to know why deep networks work and when to apply them versus traditional techniques. This balanced approach, taught by a world-class faculty from a top-tier institution, makes it an exceptional educational investment for anyone serious about a career in artificial intelligence and visual computing.
Embark on your journey to becoming a computer vision expert. Enroll in the Modern Computer Vision course and gain the knowledge to build the intelligent, seeing machines of tomorrow.
Enroll Now →