Course Overview

This course deals with the field of machine learning, which represents a modern approach to artificial intelligence. It is located between computer science, neuroscience, statistics and robotics, with applications in all areas of science and technology, medicine, economics, etc. Machine learning algorithms automate the learning process, enabling predictive and decision-making machines to improve with experience.

Learning Achievement

The participants understand statistical learning theory. They have basic experience with machine learning software, and they know how to work with data for supervised learning. They are able to apply this knowledge to new problems and data sets.

Competence

Course prerequisites

The course requires basic mathematical tools from linear algebra, calculus, and probability theory. More advanced mathematical material will be introduced as needed. The practical sessions involve programming exercises in Python. Participants need basic programming experience. They are expected to bring their own devices (laptops).

Grading Philosophy

Written Examination / 90 minutes

Course schedule

Week1: Introduction Week2: followed by Week3 to the Final Week

Course type

Online Course Requirement

Instructor

Prof. Dr. Tobias Glasmachers, Assistants

Other information

Lecture with exercise

Site for Inquiry