Optimization for Machine Learning
Data is displayed for academic year: 2023./2024.
Lecturers
Course Description
Concise introduction to modern methods for solving nonlinear optimization problems. Applications of nonlinear optimization in everyday life: from weather-forecasting to the power production management. Unconstrained optimization: line-search and trust-region methods. Constrained minimization: interior-point methods and sequential quadratic programming (SQP) methods. Numerical solutions of presented engineering problems will be solved using Matlab.
Study Programmes
Postgraduate doctoral study programme
Literature
Guanghui Lan (2021.), First-order and Stochastic Optimization Methods for Machine Learning, Springer
Suvrit Sra, Sebastian Nowozin, Stephen J. Wright (2012.), Optimization for Machine Learning, MIT Press
Charu C. Aggarwal (2020.), Linear Algebra and Optimization for Machine Learning, Springer Nature
Stephen Boyd, Stephen P. Boyd, Lieven Vandenberghe (2004.), Convex Optimization, Cambridge University Press
For students
General
ID 240528
Summer semester
6 ECTS
L3 English Level