Introduction to Statistical Machine Learning

author: Marcus Hutter, IDSIA
published: March 11, 2008,   recorded: March 2008,   views: 5282
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Description

The first part of his tutorial provides a brief overview of the fundamental methods and applications of statistical machine learning. The other speakers will detail or built upon this introduction.

Statistical machine learning is concerned with the development of algorithms and techniques that learn from observed data by constructing stochastic models that can be used for making predictions and decisions.

Topics covered include Bayesian inference and maximum likelihood modeling; regression, classification, density estimation, clustering, principal component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimization; overfitting, regularization, and validation.

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Reviews and comments:

Comment1 Kx, April 10, 2008 at 9:24 a.m.:

I think part 2 finishes about 14 min.


Comment2 Nina (staff), May 15, 2008 at 4:29 p.m.:

Thnx for your comment. We have replaced the old part 2 with a new video.


Comment3 p, May 15, 2008 at 7:21 p.m.:

Perhaps an irrelevant comment about the last slide in part 2: in the example with the cube packing the dimension, at which the central spere sticks out of the cube, seems to be d=10, not d=11. For d=9 the sphere touches the cube faces (follows from (sqrt(d)-1)/2=1).

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