9 520 - Statistical Learning Theory and Applications

Institution:

Massachusetts Institute of Technology

Subject:

Description:

Focuses on the problem of supervised and unsupervised learning from the perspective of modern statistical learning theory, starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as regularization, including support vector machines for regression and classification. Derives generalization bounds using stability. Discusses current research topics such as manifold regularization, sparsity, feature selection, bayesian connections and techniques. Discusses applications in areas such as computer vision, speech recognition, and bioinformatics. Also covers advances in the neuroscience of the cortex and their impact on learning theory and applications. Includes a final project.

Credits:

3.00

Credit Hours:

Prerequisites:

Prereq: 6.867, 6.041, 18.06, or permission of instructor

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

web.mit.edu/student/

Phone Number:

(617) 253-1000

Regional Accreditation:

New England Association of Schools and Colleges

Calendar System:

Four-one-four plan

The Course Profile information is provided and updated by third parties including the respective institutions. While the institutions are able to update their information at any time, the information is not independently validated, and no party associated with this website can accept responsibility for its accuracy.