Optimization for Machine Learning

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


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


ID 240528
  Summer semester
L2 English Level