Discriminative Learning

Graduate course to be given Winter 2008.
Course Index: 259C. Section no. 610860. Tue, Thu 12:30 - 1:50, Room 4109 in CSE Building
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Registered students will recieve a grade of Satisfactory / Unsatisfactory


A course on statistical machine learning algorithms for classification, detection and ranking. The emphasis is on discriminative, rather than generative models. The problems we will look into have a long history, however, the traditional algorithms do not work well for high dimensional data. New algorithms such as Support Vector Machines and Adaboost, prove to be very effective for high dimensional problems. So effective as to require new mathematical theory to explain their behavior. I will describe these algorithms, the theory for explaining them, and new problems and solutions that arise out of this approach. I plan to cover (some of) the following:


You should have a good foundation in math, i.e. linear algebra, probability, integrals, taylor expansions. You will also need to devote a significant amount of time between classes to reading papers from a variety of fields. Notation and terminology in these papers varies a lot, which makes reading a challange. However, mastering these different terminologies and understanding the relationships between them will provide you with an invaluable insight into this exciting and evolving research area.