Supervised Learning.
In this course, we are going to dive deeper into one of the three major workhorses empowering the success of Machine Learning: Supervised Learning. Our goal is to build agents that can improve themselves and adapt their knowledge. But what is this knowledge we want them to pick up? How should they adapt themselves? How would we know whether their adaptation led to any improvement after all? Can we have confidence in their predictions? During this course, we are going to address all of these questions and many more.
This course is about the algorithmic basics. We're going to look into the machinery that runs learning systems and the basic ideas behind them.
Do you speak Python?
Familiarity with pandas, numpy, scipy, matplotlib, will become a must! We'll cover some of it, but most will be your own responsibility!
Lecture:
- Mondays: 09:45 - 11:15 (A3.14)
Exercise:
- Group 1: Mondays: 11:45 - 13:15 (S1.29)
- Group 2: Mondays: 13:45 - 15:15 (S1.29)
Exam:
- written
- 90 min
- 5 ECTS
- at the end of this semester
- Teacher: Bayerl, Sebastian