CS6140 12F: Machine Learning

Created: Tue 04 Sep 2012
Last modified:

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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