STAT 603 - Statistical Computing and Optimization

Institution:

University of Delaware

Subject:

Statistics

Description:

Many modern statistical machine learning problems for Big Data analytics can be formulated by function optimization and linear algebraic computation. This course will provide necessary knowledge of convex optimization and matrix computation, and gain fundamental understandings of important numerical algorithms commonly used in statistical machine learning. We will emphasize on both efficient implementation and understanding for statistical computing problems. The topics to be covered include: fundamental methods for matrix and linear systems computation, matrix decomposition, convex analysis, duality and KKT conditions, 1st/2nd order methods, EM methods. Important statistical computing applications including GLM, SVM, sparsity learning, greedy function approximation, and deep neural networks will be covered. PREREQ: STAT601 and STAT602. RESTRICTIONS: Basic programming knowledge (such as R, Python, MATLAB, or C/C++) is assumed.

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.udel.edu/

Phone Number:

(302) 831-2000

Regional Accreditation:

Middle States Association of Colleges and Schools

Calendar System:

Four-one-four plan

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