Vision and Learning Lab

This course offers a comprehensive exploration of machine learning techniques for visual data (e.g., images or videos) synthesis. The course will cover a range of topics from classical algorithms (e.g., image filtering and transformation) and deep generative models (e.g., VAEs, GANs, and Diffusion Models). Participants will learn to implement image synthesis algorithms, to understand cutting-edge image synthesis techniques, and to explore intriguing research questions. This course will be of particular interest to students seeking to delve into fields of generative AI, computer vision, and deep learning.

This course offers an introduction to fundamental principles and real-world applications of 2D, 3D, and deep learning-based computer vision. Major topics include image filtering, feature detection and matching, recognition and tracking, scene understanding, camera imaging geometry, stereo vision, and deep learning-based vision. Students will learn to implement interesting computer vision algorithms in a series of well designed projects. Students will also explore intriguing research questions during a final project. This course will be of particular interest to students seeking to delve into fields of image processing and computer vision.