DS265-Deep Learning For Computer Vision

Instructors: R. Venkatesh Babu

Teaching Assistant: Badrinath Singhal and Priyam Dey

Classroom Venue: Room No. 102, CDS Department

First Class: Wednesday, 7 Jan 2026, at 05:00 PM at CDS-202 classroom

Course Registration Form: Link to Google Form Filling up the Google form is compulsory.

Teams Code (2025 DLCV): t21qooo

In the recent years, Deep Learning has pushed to boundaries of research in many fields. This course focuses on the application of Deep Learning in the field of Computer Vision. The first half of the course formulates the basics of Deep Learning, which are built on top of various concepts from Image Processing and Machine Learning. The second half highlights the various flavors of Deep Learning in Computer Vision, including Generative Models (VAE, GANs, Diffusion Models, Flow Matching), 3D reconstruction (NeRFs, 3D Gaussian Splatting etc), Recurrent Models, and Deep Reinforcement Learning Models, as well as foundational models such as Stable Diffusion.

Prerequisites

Primary crucial prerequisites : Machine Learning and Computer Vision/Image Processing

Secondary Prerequisites(familiarity preffered): Probability, Statistics and Linear Algebra.

1. Thoroughly Understanding the fundamentals of Deep Learning.
2. Gaining knowledge of the different modalities of Deep learning currently used.
3. Gaining Knowlegde about State-of the art models and Other Important Works in recent years.
4. Learning the skills to implement Deep Learning based AI Systems(Use of Multiple packages etc.)