Machine Learning 1 (Winter Term 2025/2026)

Overview

Course consisting of:
- Lectures (2 hours/week) in TRE/PHYS/E (Zellescher Weg 16) on Fridays, 09:20–10:50
- Exercise group sessions (2 hours/week)
  - VMB/0302/U, Tuesdays, 16:40–18:10
  - APB/E001/U, Thursdays, 16:40–18:10
  - APB/E001/U, Fridays, 14:50–16:20
  - APB/E001/U, Fridays, 16:40–18:10
  - Online, Thursdays, 16:40–18:10
- Self-study
- Final examination
Lecturer: Bjoern Andres
Teaching Assistants: Lucas Fabian Naumann, David Stein
Enrolment (OPAL). Additional rules for enrolment may apply, depending on the study program.

Introduction
Supervised learning
- Introduction
- Learning of binary decision trees
  - Hardness
  - Local search algorithm
- Learning of linear functions
  - Logistic regression
  - Convexity
  - Gradient descent algorithm
- Learning of composite functions (deep learning)
  - Compute graphs
  - Non-convexity
  - Forward-propagation algorithm and backward-propagation algorithm
  - Attention
  - Transformers
Semi-supervised and unsupervised learning
- Introduction
- Partitioning (clustering)
  - Clique partition problem
  - Hardness
  - Local search algorithms
- Ordering
  - Linear ordering problem
  - Hardness
  - Local search algorithms
- Classifying
  - Multi-label classification problem
Supervised structured learning
- Introduction
- Conditional graphical models
- Message passing algorithms
- Pseudo-Boolean optimization
Linear and integer optimization for machine learning
- Introduction
- Simplex algorithm
- Branch-and-bound/branch-and-cut algorithms