Machine Learning Summer School (MLSS), Canberra 2005

Machine Learning Summer School (MLSS), Canberra 2005

15 Lectures · Jan 23, 2005

About

Machine Learning is a foundational discipline of the Information Sciences. It combines deep theory from areas as diverse as Statistics, Mathematics, Engineering, and Information Technology with many practical and relevant real life applications. The aim of the summer school is to cover the entire spectrum from theory to practice. It is mainly targeted at research students, IT professionals, and academics from all over the world.

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Lectures

06:39:02

Probabilistic Graphical Models

Sam Roweis

Feb 25, 2007

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154208 Views

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03:03:31

How to predict with Bayes, MDL, and Experts

Marcus Hutter

Feb 25, 2007

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7555 Views

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01:49:01

Generalized Principal Component Analysis (GPCA)

René Vidal

Feb 25, 2007

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19382 Views

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01:53:54

Text Categorization

Jon David Patrick

Feb 25, 2007

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5747 Views

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05:44:22

Independent Component Analysis

Aapo Hyvärinen

Feb 25, 2007

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73296 Views

Tutorial
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03:59:04

Gradient Methods for Machine Learning

Nicol Schraudolph

Feb 25, 2007

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9968 Views

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03:17:29

Exponential Families in Feature Space

Alex Smola

Feb 25, 2007

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7665 Views

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47:31

Exponential Families in Feature Space - Part 5

S.V.N. Vishwanathan

Feb 25, 2007

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4051 Views

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59:27

Exponential Families in Feature Space - Part 6

S.V.N. Vishwanathan

Feb 25, 2007

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4036 Views

Lecture
03:15:09

Kernel Methods for Higher Order Image Statistics

Matthias O. Franz

Feb 25, 2007

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11208 Views

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03:10:51

MarkusSparse Grid Methods

Markus Hegland

Feb 25, 2007

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6398 Views

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01:31:32

Reinforcement Learning

Douglas Aberdeen

Feb 25, 2007

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7220 Views

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02:32:26

Graph Matching Algorithms

Terry Caelli

Feb 25, 2007

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20360 Views

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05:44:47

Machine Learning for Games

Thore Graepel

Feb 25, 2007

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16221 Views

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03:22:36

Bioinformatics Challenge: Learning in Very High Dimensions with Very Few Samples

Adam Kowalczyk

Feb 25, 2007

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7471 Views

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