Machine Learning I - Theoretical Foundations (4V+2U, 8.0 LP)
|SWS||Type||Course Form||CP (Effort)||Presence-Time / Self-Study|
|-||K||Lecture with exercise classes (V/U)||8.0 CP||156 h|
|2||U||Exercise class (in small groups)||28 h|
|(4V+2U)||8.0 CP||84 h||156 h|
|CP, Effort||8.0 CP = 240 h|
|Position of the semester||1 Sem. in SuSe|
|Level|| Master (Entry Level)|
|Area of study||[INF-KI] Intelligent Systems|
Possible Study achievement
- Verification of study performance: proof of successful participation in the exercise classes (ungraded)
- Details of the examination (type, duration, criteria) will be announced at the beginning of the course.
- Introduction and Overview
- Linear classifiers
- Support vector machines
- Kernel methods
- Deep learning
- Regularization and Overfitting
- Dimensionality reduction
- Random forests
Relevant literature (in order of descending importance):
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning. MIT Press, 2017.
- Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.
- John Shawe-Taylor and Nello Cristianini. Kernel methods for pattern analysis. Cambridge university press, 2004.
- Latex slides and blackboard writing
- Active learning approaches in class
- Slides and blackboard pictures as download (PDF)