Most of the research in machine learning has been directed to the problem of binary classification in which the learned classifier outputs one of two possible answers. This is a fundamental problem, but still it does not fit well important real-world applications. In this tutorial we will focus on more complex settings in which there are many possible answers with complex preference relationships among them. Notable examples include multi-class categorization, hierarchical classification, and sequence prediction.

We will use the algorithmic framework of online learning for several reasons. First, in general online algorithms are conceptually simple and easy to implement. Furthermore, online algorithms process one example at a time. Thus, such methods are appealing for large data sets. Second, online algorithms have been used in practice for the applications that we will use as examples. Third, the analysis of these algorithms is based on mathematical tools which are simpler than those needed for analyzing other types of algorithms.

The goals of the tutorial :


Schedule and Dates



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