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.
Videos
Introduction

Introduction to the MLSS 2008
Mar 11, 2008
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6042 views
Lectures

Learning in Computer Vision
May 5, 2008
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53193 views

Machine Learning Laboratory
May 7, 2008
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7294 views

Contrast Data Mining: Methods and Applications
Mar 12, 2008
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9222 views

Introduction to Statistical Machine Learning
Mar 11, 2008
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30759 views

Online Learning, Regret Minimization, and Game Theory
May 7, 2008
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26252 views

Latent Variable Models for Document Analysis
Mar 11, 2008
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10482 views

Inference in Graphical Models
Mar 12, 2008
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15091 views

Monte Carlo Simulation for Statistical Inference, Model Selection and Decision M...
Mar 13, 2008
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152254 views

Foundations of Machine Learning
Mar 11, 2008
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14208 views

Machine Learning Laboratory
May 7, 2008
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6286 views

Kernel methods and Support Vector Machines
Mar 11, 2008
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81019 views

Introduction to Reinforcement Learning
Mar 17, 2008
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36882 views