Machine Learning Summer School 2003 - Tuebingen

Statistical Learning Theory

author: Olivier Bousquet, Google

Description

This course will give a detailed introduction to learning theory with a focus on the classification problem. It will be shown how to obtain (pobabilistic) bounds on the generalization error for certain types of algorithms. The main themes will be: * probabilistic inequalities and concentration inequalities * union bounds, chaining * measuring the size of a function class, Vapnik Chervonenkis dimension, shattering dimension and Rademacher averages * classification with real-valued functions Some knowledge of probability theory would be helpful but not required since the main tools will be introduced.

Categories

Top: Computer Science: Machine Learning: Statistical Learning
Top: Computer Science: Machine Learning

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*Slides*
0:01	Statistical Learning Theory
0:35	Roadmap (1)
2:21	Roadmap (2)
2:49	Lecture 1
3:28	Learning and Inference
4:51	Pattern recognition
6:09	Approximation/Interpolation
8:48	Occam’s Razor
10:52	No Free Lunch
13:34	Assumptions
14:28	Goals
15:39	Probabilistic Model
17:56	Probabilistic Model
19:43	Probabilistic Model
23:26	Target function
29:34	Assumptions about P
32:41	Approximation/Interpolation (again)
33:10	Overfitting/Underfitting
34:03	Empirical Risk Minimization
35:25	Approximation/Estimation
38:13	Structural Risk Minimization
40:15	Regularization
41:41	Bounds (1)
42:42	Bounds (2)

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