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Regular version of the site

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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