Optimization for Machine Learning

Data is displayed for academic year: 2023./2024.

Lecturers

Prof.
Josipa Pina Milišić

Course Description

This course provides an overview of modern optimization methods for applications in machine learning and data science. In the introductory part of the course, students are introduced to the basic concepts of convex analysis. An overview of convex optimization problems is given, with particular attention to examples of convex problems encountered in machine learning. The first part of the course is devoted to gradient methods. The central part of the course deals with the concept of duality and its applications. The last part of the course deals with constrained optimization algorithms such as penalty and barrier methods, and interior point methods. The implementation of the program solutions is done with the Python.

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