Machine Learning Summer School (MLSS), Kioloa 2008

Machine Learning Summer School (MLSS), Kioloa 2008

13 Lectures · Mar 3, 2008

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 research students, 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. **For 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. Organizers, this summer school is organized by the Computer Sciences Laboratory of the Australian National University (CSL@ANU) and the Statistical Machine Learning program of the National ICT Australia (SML@NICTA), jointly with support from the Max-Planck-Institute for Biological Cybernetics in Tübingen and the Pascal Netwok. Please visit www.mlss.cc for more information about the previous summer schools. Local organizers are Li Cheng, Marcus Hutter, and Alex Smola.

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Uploaded videos:

Introduction

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02:44

Introduction to the MLSS 2008

Marcus Hutter

Mar 11, 2008

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6041 Views

Introduction

Lectures

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02:25:54

Introduction to Statistical Machine Learning

Marcus Hutter

Mar 11, 2008

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30741 Views

Tutorial
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05:12:03

Kernel methods and Support Vector Machines

Alex Smola

Mar 11, 2008

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81000 Views

Tutorial
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05:22:55

Monte Carlo Simulation for Statistical Inference, Model Selection and Decision M...

Nando de Freitas

Mar 13, 2008

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152233 Views

Tutorial
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04:58:56

Latent Variable Models for Document Analysis

Wray Buntine

Mar 11, 2008

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10467 Views

Tutorial
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05:47:41

Introduction to Reinforcement Learning

Csaba Szepesvári

Mar 17, 2008

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36863 Views

Tutorial
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02:56:13

Foundations of Machine Learning

Marcus Hutter

Mar 11, 2008

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14195 Views

Tutorial
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05:39:26

Inference in Graphical Models

Tibério Caetano

Mar 12, 2008

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15081 Views

Tutorial
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05:38:38

Contrast Data Mining: Methods and Applications

Rao Kotagiri

Mar 12, 2008

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9208 Views

Tutorial
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05:31:20

Learning in Computer Vision

Simon Lucey

May 05, 2008

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53169 Views

Tutorial
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05:27:46

Online Learning, Regret Minimization, and Game Theory

Avrim Blum

May 07, 2008

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26240 Views

Tutorial
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02:22:36

Machine Learning Laboratory

Christfried Webers

May 07, 2008

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7285 Views

Tutorial
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09:54

Machine Learning Laboratory

S.V.N. Vishwanathan

May 07, 2008

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6283 Views

Tutorial