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The goal of this class is to familiarize you with various state-of-the-art machine learning techniques for classification, regression, clustering and dimensionality reduction. Besides this, an important aspect this class is to provide a modern statistical view of machine learning.

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The course includes (informed and uninformed) search, constraint satisfaction, optimization, games, propositional and first order logic, probability and learning.

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This course introduces the students to one of the most influential developments in modern machine learning, namely kernel methods. The course will be focused on familiarizing the student with a number of practical kernel-based algorithms (such as “support vector machines”, “kernel Fisher Discrimination”, “kernel principal components anal