The course gives an introduction to Artificial Intelligence (AI). Starting from general concepts and a brief discussion of what AI is, we will mainly focus on aspects of machine learning (ML). This will include a discussion of which problems are suitable for ML, how the learning data may influence the selection of the learning model and the outcome of the learning process, etc. As far as possible, practical examples will be given.

For positive completion of the course, the following subaspects need to be fulfilled:

1) Class room attendance is required. Due to Corona virus, all classes will be held as online meetings via Zoom. The Zoom link will be communicated via e-mail (Base) the day before the class. (One Link for all classes.)

2) Student presentations regarding selected questions from the subject matter will be requested during the semester. The work should be done in small groups whenever possible. Presentations will be given during the Zoom classes.

3) Positive result in end of term test: At the last session (17.6.2020) each working group will present their machine learning experiment and resulting learnings (approx. 5 mins talks) to the plenary. In addition, each group will have published by June 17 a blogpost on cdslab.uni-ak.ca.at describing their ML experiment and outcome in more detail. More details on the blog publication functionality will be given during the session on June 5.

Students from other faculties or universities will be given a place on the course subject to room capacities.