Neural Networks Course: Computer Vision & NLP with Prof. Arijit Sur | IIT Guwahati
Course Details
| Exam Registration | 1568 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering, Artificial Intelligence |
| 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 | 25 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Unlock the Future of AI: A Comprehensive Guide to Neural Networks for Vision and Language
The fields of Computer Vision (CV) and Natural Language Processing (NLP) are revolutionizing our world. From self-driving cars and medical image analysis to real-time language translation and intelligent chatbots, the applications are boundless. For students and professionals aiming to be at the forefront of this technological wave, a strong foundation in the underlying neural network architectures is non-negotiable.
We are excited to present a detailed overview of a premier 12-week course, "Neural Networks for Computer Vision and Natural Language Processing," instructed by Prof. Arijit Sur from IIT Guwahati. This course is meticulously designed to transform beginners into proficient practitioners, ready to tackle real-world AI challenges.
Meet Your Instructor: A Pioneer in Deep Learning Research
The course is led by Prof. Arijit Sur, a full professor in the Department of Computer Science and Engineering at IIT Guwahati. With a Ph.D. from IIT Kharagpur and over a decade of teaching and research experience, Prof. Sur is a leading authority in applying deep learning to complex problems.
His research, conducted through the "Multimedia Lab" he founded, spans critical areas such as:
- Image & Video Restoration and Super-Resolution
- Multi-modal Image-Text Analysis
- Semantic Segmentation & Change Detection in satellite/medical imaging
- Self-supervised Learning, Zero-shot Learning, and Robotic Vision
With over 90 publications in top-tier forums and a role as an Associate Editor for the Multimedia Systems Journal, Prof. Sur brings cutting-edge research insights directly into the curriculum.
Who Should Enroll in This Neural Networks Course?
This course is crafted to deliver value across the spectrum of learners in the AI ecosystem:
- Students (B.Tech/M.Tech/PhD): Gain a robust theoretical and practical foundation, significantly enhancing your research potential and employability in top AI-driven companies.
- Industry Beginners: Bridge the gap between academic theory and industry application. Learn to implement state-of-the-art models for vision and language tasks.
- Faculty & Researchers: Stay updated with the latest tools and advancements in deep learning to enrich your teaching curriculum and guide cutting-edge research projects.
Detailed 12-Week Course Curriculum: From Fundamentals to Frontiers
The course follows a structured progression, ensuring a smooth learning curve from basic concepts to advanced architectures.
| Week | Core Topics | Key Learning Outcomes |
|---|---|---|
| Weeks 1-2 | ML Fundamentals, Deep Neural Networks | Understand core ML concepts, optimization, and the basics of deep learning. |
| Weeks 3-4 | Computer Vision, CNN Architectures | Master Convolutional Neural Networks (CNNs), ResNets, and their application to image data. |
| Week 5 | Sequential Modelling, RNNs | Learn Recurrent Neural Networks (RNNs) for sequence data like text and time series. |
| Weeks 6-7 | Generative Models (VAE, GAN, Diffusion) | Explore how AI can create new data, from images to text, using the latest generative models. |
| Week 8 | Transformers & Large Language Models (LLMs) | Dive into the architecture behind models like GPT, understanding self-attention and language modeling. |
| Week 9 | Zero-Shot & Few-Shot Learning | Learn how models can recognize or classify with very little or no labeled training data. |
| Weeks 10-11 | Image Enhancement, Classification, Object Detection | Apply DL to practical CV tasks: improving image quality, identifying objects, and medical image analysis. |
| Week 12 | Multimodal & Self-Supervised Learning | Combine vision and language models, and learn powerful paradigms for learning from unlabeled data. |
Essential Learning Resources
The course is supported by authoritative textbooks to deepen your understanding:
Primary Textbooks:
- Neural Networks and Learning Machines by Simon Haykin
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (the definitive "Deep Learning Bible")
Reference Books:
- Deep Learning for Computer Vision by Shanmugamani Rajalingappaa
- Deep Learning with TensorFlow by Zaccone Giancarlo
Why This Course is a Career Catalyst
The curriculum is directly aligned with the massive demand in the AI/ML industry. Companies are actively seeking professionals skilled in:
- Designing and training CNN models for visual recognition systems.
- Building and fine-tuning Transformer-based models and LLMs for NLP applications.
- Implementing generative AI solutions for content creation and data augmentation.
- Developing multimodal systems that understand both images and text.
By mastering the concepts covered in this 12-week journey, you will not just learn algorithms—you will develop the mindset to solve tomorrow's AI problems. Whether your goal is to contribute to groundbreaking research, land a coveted role in tech, or lead AI innovation in your field, this course provides the knowledge and credibility from one of India's premier institutions to get you there.
Take the first step towards mastering the intelligent systems of the future.
Enroll Now →