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.
Videos
Introduction

Introduction
Feb 25, 2007
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3116 views
Lectures

Exponential Families in Feature Space
Feb 25, 2007
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4967 views

The Sparse Grid Method
Feb 25, 2007
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9376 views

Exponential Families
Feb 25, 2007
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20974 views

Policy-gradient Reinforcement Learning
Feb 25, 2007
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11111 views

Graphical Models for Structural Pattern Recognition
Feb 25, 2007
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12816 views
Optimization for Kernel Methods
Feb 25, 2007
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6795 views

Dirichlet Processes and Nonparametric Bayesian Modelling
Feb 25, 2007
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32279 views

Learning techniques in Planning
Feb 25, 2007
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8945 views

Introduction to Learning Theory
Feb 25, 2007
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30930 views

Reinforcement Learning
Feb 25, 2007
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29325 views
Anti-Learning
Feb 25, 2007
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8437 views
Rapid Stochastic Gradient Descent: Accelerating Machine Learning
Feb 25, 2007
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10075 views

Brain Computer Interfaces
Feb 25, 2007
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17308 views

Information Retrieval and Text Mining
Feb 25, 2007
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15733 views

Measures of Statistical Dependence
Feb 25, 2007
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11052 views

Learning with Kernels
Feb 25, 2007
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13552 views