CS 7300 - Unsupervised Feature Learning

Credits: 3

The course covers the principles and advances in unsupervised feature learning algorithms. It focuses on development of machine learning features, considering the feature hierarchies from unlabeled data. The learning algorithms are exploited in many applications. Topics include clustering, sparse coding, Boltzmann machine, autoencoders, and deep belief networks. The course also requires an open-ended research project. Prerequisite: CS 5200 and STAT 5020, or permission of instructor. Credit cannot be received for both DATA 7300 and CS 7300. Approved for distance education.

Repeatable:
No