LEARNINGClass 18: Local Methods
Instructor: Sasha Rakhlin
Description
We start by discussing the bias-variance trade-off, and then go on to discussing local methods: k-Nearest Neighbors and Nadaraya-Watson, before finishing with a discussion of interpolation.
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
Suggested 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.