In this lecture, the students will be introduced into the most frequently used machine learning methods in computer vision and robotics applications. The major aim of the lecture is to obtain a broad overview of existing methods, and to understand their motivations and main ideas in the context of computer vision and pattern recognition. Also, in addition to the standard methods, the lecture will also cover some recent topics such as CRFs, Random Forests, and IVMs.

Schedule:

- Introduction

- Regression

- Probabilistic Graphical Models

- Boosting

- Kernel Methods

- Gaussian Processes

- Evaluation and Model Selection

- Sampling Methods

- Clustering