Statistical Methods for Machine Learning

The course describes and analyzes, in a rigorous statistical framework, some of the most important machine learning techniques. This will provide the student with a rich set of conceptual and methodological tools for understanding the general phenomenon of learning in machines.

Upon completion of the course students will be able to:
1. understand the notion of overfitting and its role in controlling the statistical risk
2. describe some of the most important machine learning algorithms and explain how they avoid overfitting
3. run machine learning experiments using the correct statistical methodology
These objectives are measured via a combination of two components: the project report and the oral discussion. The final grade is formed by assessing the project report, and then using the oral discussion for fine tuning.