Statistical Learning: First Steps
9.520/6.860, Class 12
Instructor: Sasha Rakhlin
Description
We reintroduce the setting of statistical learning and then go on to discuss the no-free lunch theorem and the perceptron algorithm.
Slides
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 2 – Foundational Results
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
- L. Devroye, L. Gyorfi, G. Lugosi, A Probabilistic Theory of Pattern Recognition, Springer, 1996.
- O. Bousquet, S. Boucheron, G. Lugosi Introduction to Statistical Learning Theory, Advanced Lectures on Machine Learning pp 169-207.