MIT Subject Listing & Schedule
Fall 2017 Search Results

Searched for: "9.520"    Subjects offered any term

1 subject found.

9.520[J] Statistical Learning Theory and Applications

()
(Same subject as 6.860[J])
Prereq: 6.867, 6.041B, 18.06, or permission of instructor
Units: 3-0-9
URL: http://web.mit.edu/9.520/www/
Lecture: MW1-2.30 (46-3310)

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.
T. Poggio, L. Rosasco
No required or recommended textbooks