Statistical Machine Learning
Introductory course on supervised learning — classification and regression with real data — and its computational and statistical foundations.
Instructor: Jens Sjölund, Sara Hamis, Thomas B. Schön, Antônio H. Ribeiro, and others
Overview
This master’s course (1RT700, 5 hp) focuses on supervised learning, i.e., classification and regression with real data, and its computational and statistical foundations. The course combines theoretical content with practical Python-based sessions.
Format
- 10 lectures
- 10 exercise sessions
- 1 computer lab
- 1 mini-project
- Written final exam
Prerequisites
Elementary calculus, linear algebra, probability theory (gradients, matrix operations, probability distributions, expected values, conditional probability), and basic Python programming.
Textbook
Machine Learning — A First Course for Engineers and Scientists, available free online.