Machine Learning 2 (Summer Term 2025)

Overview

• Course (2/2/0) consisting of:
  • Lectures in APB/E023/U (Nöthnitzer Str. 46) on Mondays, 11:10–12:40
  • Exercises in APB/E023/U on Tuesdays, 11:10–12:40, starting 2025-04-22
  • Self-study
  • Final Examination
• Lecturer: Bjoern Andres
• Teaching Assistants: Jannik Irmai, Jannik Presberger, David Stein
• Enrolment (OPAL). Additional rules for enrolment may apply, depending on the study programme.

Contents

This course is about selected, not necessarily connected topics of machine learning research and their application in the field of biomedical image analysis:

• Introduction
• Recap of Machine Learning I
  • Supervised deep learning
  • Unsupervised and semi-supervised learning
  • Supervised structured learning
• Deep learning
  • Attention
  • Transformers
• Partial optimality and machine learning
  • Mathematical foundations
  • Clustering
  • Ordering
  • Graphical model inference
• Integer linear optimization and machine learning
  • Mathematical foundations
  • Clustering
  • Ordering
  • Graphical model inference
• Machine learning for biomedical image analysis
  • Classification and the CellMap Challenge
  • Clustering and image segmentation for connectomics
  • Quadratic assignment and the recognition of organoids

Exercises