Machine Learning Summer School (MLSS), Canberra 2006

Machine Learning Summer School (MLSS), Canberra 2006

17 Lectures · Feb 5, 2006

About

This school is suitable for all levels, both for people without previous knowledge in Machine Learning, and those wishing to broaden their expertise in this area. It will allow the participants to get in touch with international experts in this field. Exchange of students, joint publications and joint projects will result because of this collaboration. \ For a research student, the summer school provides a unique, high-quality, and intensive period of study. It is ideally suited for students currently pursuing, or intending to pursue, research in Machine Learning or related fields. Limited scholarships are available for students to cover accommodation and registration costs. If funds are available partial travel support might also be provided. \ IT professionals who use Machine Learning will find that the summer school provides relevant knowledge and exposure to contemporary techniques. In addition, they will benefit by direct interaction with top-notch researchers and knowledge workers. Previous experience indicates that personnel from both the industry as well as national laboratories like CSIRO, DSTO benefit immensely from the school. \ For academics, the summer school is an excellent opportunity to help getting started in research on novel topics in Machine Learning. It provides an ideal forum for networking and discussions. Academics will also benefit from interaction with IT professionals which will lead to a deeper understanding of real life problems.

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Uploaded videos:

Introduction

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04:58

Introduction

Douglas Aberdeen

Feb 25, 2007

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3112 Views

Introduction

Lectures

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04:15:17

Learning with Kernels

Bernhard Schölkopf

Feb 25, 2007

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Lecture
01:51:44

Rapid Stochastic Gradient Descent: Accelerating Machine Learning

Nicol Schraudolph

Feb 25, 2007

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Lecture
01:26:53

Graphical Models for Structural Pattern Recognition

Tibério Caetano

Feb 25, 2007

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Lecture
04:30:52

Optimization for Kernel Methods

Sathiya Keerthi

Feb 25, 2007

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Lecture
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05:18:54

Brain Computer Interfaces

Klaus-Robert Müller

Feb 25, 2007

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Lecture
04:58:59

Reinforcement Learning

Satinder Singh

Feb 25, 2007

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29259 Views

Tutorial
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01:39:03

Exponential Families in Feature Space

S.V.N. Vishwanathan

Feb 25, 2007

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Lecture
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04:11:39

Exponential Families

Alex Smola

Feb 25, 2007

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Tutorial
01:31:23

Anti-Learning

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Feb 25, 2007

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Learning techniques in Planning

Rao Kambhampati

Feb 25, 2007

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Lecture
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01:02:16

Measures of Statistical Dependence

Arthur Gretton

Feb 25, 2007

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01:20:19

Policy-gradient Reinforcement Learning

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Lecture
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The Sparse Grid Method

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Lecture
03:57:48

Introduction to Learning Theory

Olivier Bousquet

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Tutorial
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Dirichlet Processes and Nonparametric Bayesian Modelling

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Tutorial
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Information Retrieval and Text Mining

Thomas Hofmann

Feb 25, 2007

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15721 Views

Lecture