Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Course INF-75-50-K-5

Machine Learning I - Theoretical Foundations (4V+2U, 8.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K Lecture with exercise classes (V/U) 8.0 CP 156 h
4 V Lecture 56 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 [5] Master (Entry Level)
Language [EN] English
Area of study [INF-KI] Intelligent Systems
Livecycle-State [NORM] Active

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
  • Optimization
  • Kernel methods
  • Deep learning
  • Regularization and Overfitting
  • Regression
  • Clustering
  • 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)

Requirements for attendance (informal)


Requirements for attendance (formal)


References to Course [INF-75-50-K-5]

Module Name Context
[INF-75-50-M-5] Machine Learning I - Theoretical Foundations P: Obligatory 4V+2U, 8.0 LP
[SO-09-120-M-6] Interdisciplinary cross-section WP: Obligation to choose 4V+2U, 8.0 LP
Course-Pool Name
[INF-KI_V-KPOOL-6] Lectures of the teaching area Intelligent Systems