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
Probabilistic Graphical Models
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How to predict with Bayes, MDL, and Experts
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Generalized Principal Component Analysis (GPCA)
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Text Categorization
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Independent Component Analysis
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Gradient Methods for Machine Learning
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Exponential Families in Feature Space
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Exponential Families in Feature Space - Part 5
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Exponential Families in Feature Space - Part 6
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Kernel Methods for Higher Order Image Statistics
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MarkusSparse Grid Methods
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Reinforcement Learning
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Graph Matching Algorithms
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Machine Learning for Games
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Bioinformatics Challenge: Learning in Very High Dimensions with Very Few Samples
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