Optimization in Machine Learning
In this course we consider different approaches to optimizing criterion functions arising in machine learning problems. In particular we address the following topics: general-purpose methods of one- and multi- dimensional optimization; bounding methods, constraint optimization problems including primal-dual algorithms; interior-point methods; cutting-plane methods; stochastic optimization; alrotighms for optimizing sparse linear models for regression and classification.
Lecturer:
Dmitry Kropotov
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