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. Deep learning is currently the most important technique in computer vision.
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.
This is a graduate level module on some advanced topics in computer vision.