The scientific discipline of Machine Learning is concerned with algorithmic paradigms and techniques that allow machines to learn from experience. Given the vast quantities of data that are collected in the modern world, Machine Learning has become increasingly important in order to utilize the knowledge inherent in this data. In this graduate course, we will examine, in depth, a variety of techniques used in machine learning and data mining and also examines issues associated with their application. Topics include algorithms for supervised learning including decision trees, artificial neural networks, probabilistic methods, boosting, and support vector machines; and unsupervised learning including clustering and principal components analysis. Also covers computational learning theory and other methods for analyzing and measuring the performance of learning algorithms. The course is largely self-contained.
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