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Machine Learning Summer School 2007 - Tuebingen

Dirichlet Processes: Tutorial and Practical Course

author: Yee Whye Teh , University College London

Description

The Bayesian approach allows for a coherent framework for dealing with uncertainty in machine learning. By integrating out parameters, Bayesian models do not suffer from overfitting, thus it is conceivable to consider models with infinite numbers of parameters, aka Bayesian nonparametric models. An example of such models is the Gaussian process, which is a distribution over functions used in regression and classification problems. Another example is the Dirichlet process, which is a distribution over distributions.

Dirichlet processes are used in density estimation, clustering, and nonparametric relaxations of parametric models. It has been gaining popularity in both the statistics and machine learning communities, due to its computational tractability and modelling flexibility. In the tutorial I shall introduce Dirichlet processes, and describe different representations of Dirichlet processes, including the Blackwell-MacQueen? urn scheme, Chinese restaurant processes, and the stick-breaking construction. I shall also go through various extensions of Dirichlet processes, and applications in machine learning, natural language processing, machine vision, computational biology and beyond.

In the practical course I shall describe inference algorithms for Dirichlet processes based on Markov chain Monte Carlo sampling, and we shall implement a Dirichlet process mixture model, hopefully applying it to discovering clusters of NIPS papers and authors.

Categories

Top: Computer Science: Machine Learning: Bayesian Learning
Top: Computer Science: Machine Learning

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*Slides*
0:01	Dirichlet Processes: Tutorial and Practical Course
1:34	Dirichlet Processes
3:55	Outline-part01
4:34	Outline-part02
4:37	Function estimation-part01
6:42	Function estimation-part02
7:41	Function estimation-part03
8:08	Density estimation-part01
10:11	Density estimation-part02
11:08	Density estimation-part03
11:26	Density estimation-part04
13:26	Density estimation-part05
14:40	Density estimation-part06
15:26	Semiparametric modelling-part01
17:55	Semiparametric modelling-part02
19:47	Model selection/averaging-part01
21:34	Model selection/averaging-part02
21:41	Model selection/averaging-part03
22:03	Model selection/averaging-part04
23:54	Model selection/averaging-part05
24:05	Model selection/averaging-part04A
28:10	Model selection/averaging-part05A
28:35	Model selection/averaging-part06
30:25	Model selection/averaging-part07
31:32	Model selection/averaging-part08
33:44	Model selection/averaging-part09
34:36	Outline-part03
34:52	Finite mixture models
36:15	Infinite mixture models
39:46	Gaussian processes-part01
40:19	Gaussian processes-part02
41:10	Gaussian processes-part03
41:45	Dirichlet processes-part01
43:25	Dirichlet processes-part02
43:33	Dirichlet processes-part01A
43:47	Dirichlet processes-part02A
45:44	Dirichlet processes-part03
47:09	Dirichlet processes-part04
48:25	Dirichlet processes-part05
51:34	Dirichlet processes-part06
54:28	Dirichlet processes-part07
55:28	Dirichlet processes-part08
56:23	Dirichlet processes-part09
56:46	Dirichlet processes-part10
57:38	Dirichlet processes-part11
57:56	Dirichlet processes-part12
58:13	Dirichlet processes-part13
58:16	Outline-part04

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