This module is about statistical machine learning covering basic probabilistic inferences, linear models, kernel methods, graphical models, approximate inference, sampling methods, and sequential data.
The goal of computer vision is to enable computers to recognize the world through visual information, such as, images or videos. This module focuses on the probabilistic framework of computer vision that is useful to understand visual information.
This module focuses on algorihtm analysis, namely the computational complexity of recurrent algorithms, randomized algorithms, amortized algorithms, etc. Aside from analysis, it also covers both fundamental and advanced algorithms/data structures.
In capstone projects, you will have the chance to design an individual project —a project that can build on your independent research experience.
This is a graduate level module on some advanced topics in computer vision.