Statistical Methods for Machine Learning

The course describes, in a rigorous statistical framework, some fundamental ideas and techniques behind the design and analysis of machine learning algorithms.

Upon completion of the course, students will be able to: understand the notion of overfitting and its role in controlling the statistical risk, describe some of the most fundamental machine learning algorithms explaining how they avoid overfitting, run machine learning experiments using the correct statistical methodology. The project report and the oral discussion measure the achievement of these objectives. The grade for the project report and the grade for the written exam are combined to compute the final grade for the course.