Machine Learning Summer School 2006 - Taipei

Kernel Methods

author: Alexander J. Smola, Australian National University

Description

In this short course I will discuss exponential families, density estimation, and conditional estimators such as Gaussian Process classification, regression, and conditional random fields. The key point is that I will be providing a unified view of these estimation methods. In the second part I will discuss how moment matching techniques in Hilbert space can be used to design two-sample tests and independence tests in statistics. I will describe the basic principles and show how they can be used to correct covariate shift, select features, or merge databases.

Categories

Top: Computer Science: Machine Learning: Kernel Methods

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*Slides*
0:00	Kernel Methods - Lecture 1: Estimation in Exponential Families
0:12	Priors
0:44	Tossing a dice with priors
2:10	Conjugate Priors
10:11	Example: Multinomial Distribution
13:14	Optimization Problems
14:38	Mini Summary
16:28	Graphical Model
19:27	Conditional Independence
21:00	Cliques
24:27	Hammersley Clifford Theorem
27:19	Proof
28:42	Example: Normal Distributions
30:28	Example: Normal Distributions 01
33:13	Computing g()
36:49	Forward Backward Algorithm
38:14	Message Passing
41:54	Message Passing 01
47:43	Junction Trees
50:07	Junction Tree Algorithm
51:48	Example
52:39	Problems
54:40	Recall: Optimization
55:52	Mini Summary

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