Students will obtain an in-depth introduction to unsupervised learning methods: models, theories, and application examples.

Unsupervised Learning models, such as self-organizing maps, growing hierarchical structures, cellular automata, ant colony optimisation, ...

Lecture Dates:

• 9.10..:  Introduction;
• 16.10.: Genetic Algorithms, Cellular Automata, Swarms
• 23.10.: Multi-Agent Systems
• 30.10.: (back-up slot)
• 6.11.:   SOM 1: Basics, Architecture, Training Process
• 13.11.: SOM 2: Visualizations: class distributions, densities, cluster boundaries, topology violations
• 20.11.: SOM 3: visualizations (cont.), Quality Measures,  SOM Comparisons,
• 27.11.: SOM 4: related Architectures
• 4.12.:   (back-up slot)
• 11.12.:   Swarm Systems 1
  
• 18.12.:   Swarm Systems 2

ECTS/Effort:

Lectures: 8 sessions a 2h: 16h

Literature: 10h

Exercises:

    Ex1: 20h

    Ex2: 20h

    Ex3: 20h

Exam Preparation: 25.5h

Exam: 1h

SUM: 112.5h