9.520 Statistical Learning Theory and Applications
Provides students with the knowledge needed to use and develop advanced machine learning solutions to challenging problems. Covers foundations and recent advances of machine learning in the framework of statistical learning theory. Focuses on regularization techniques key to high-dimensional supervised learning. Starting from classical methods such as regularization networks and support vector machines, addresses state-of-the-art techniques based on principles such as geometry or sparsity, and discusses a variety of algorithms for supervised learning, feature selection, structured prediction, and multitask learning. Also focuses on unsupervised learning of data representations, with an emphasis on hierarchical (deep) architectures.
Lecture occurs 1:00 PM to 2:30 PM on Mondays and Wednesdays in 46-3310.
This class counts for a total of 12 credits.
MIT 9.520 Statistical Learning Theory and Applications Related Textbooks
MIT 9.520 Statistical Learning Theory and Applications On The Web
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