9.520/6.860, Class 02

Class 02: The Learning Problem and Regularization

Instructor: Lorenzo Rosasco


We formalize the problem of learning from examples in the framework of statistical learning theory and introduce key terms and concepts such as loss functions, empirical and excess risk, generalization error and consistency. We briefly describe foundational results and introduce the concepts of hypothesis space and regularization.


Slides for this lecture: PDF.

Class Reference Material

L. Rosasco, T. Poggio, Machine Learning: a Regularization Approach, MIT-9.520 Lectures Notes, Manuscript, Dec. 2017

Chapter 1 - Statistical Learning Theory

Note: The course notes, in the form of the circulated book draft is the reference material for this class. Related and older material can be accessed through previous year offerings of the course.

Further Reading