First session will be Friday October 4th, 10-12 hrs, in room 306 house 6 (Cramér room), and the course will then run with one session per week until mid-December.

The main text for the course will be "Statistical Learning with Sparsity: The Lasso and Generelizations" by Hastie, Tibshirani and Wainwright ( https://web.stanford.edu/~hastie/StatLearnSparsity/ )

The course is planned to cover most of Chapters 1--8 of this book. Additional material covering basics on convex sets, functions and convex optimisation will also be included, as well as additional material on proximal algorithms for solving convex optimisation problems relavant to the course contents.

The course will have a hands-on perspective, solving exercises and doing computer assignments, rather than plunging deep into theory.

Prerequisites are multivariable calculus, linear algebra, basic knowledge of optimisation, statistics including regression and preferrably logistic regression.

- Teacher: Tobias Rydén