Introduction to Machine Learning

The course gives a basic introduction to data analysis and machine learning. It covers the learning regimes of supervised and unsupervised learning thoroughly, and a light introduction to reinforcement learning and explanation methods for machine learning models. The course work is project driven with focus on applications, using Python and commonly used machine learning libraries.

Knowledge: Fundamentals of machine learning with commonly used learning algorithms.

Skills: Ability to analyse data sets, and train and evaluate machine learning models on data. Evaluate adequateness of learning regimes based on the data.

General competencies: Understand the basic principles of data analysis and machine learning. Knowledge about the applicability and limitations of different contemporary learning algorithms.

Lectures, self-study. Compulsory activity in the form of assignments, will be published during the semester. These must be passed to gain admittance to the final exam.