Analysis of various optimization methods and their application in training common machine learning models. A brief review of gradient descent, line search, Newton's method, and Python programming. Specific topics include: quasi-Newton method, proximal gradient, duality, momentum, stochastic gradient descent, and variance reduction. Prerequisites: Math 449 or permission of instructor.
Course Attributes: FA NSMAR NSMAS NSM
Section 01Topics in Applied Mathematics
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