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:

• 8.10.:  Introduction;
• tba: Genetic Algorithms, Cellular Automata, Swarms
• tba: Multi-Agent Systems
• tba: (back-up slot)
• tba:   SOM 1: Basics, Architecture, Training Process
• tba: SOM 2: Visualizations: class distributions, densities, cluster boundaries, topology violations
• tba SOM 3: visualizations (cont.), Quality Measures,  SOM Comparisons, related Architectures
• tba:   Swarm Systems 1
  
• tba:   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

Exercises and written exam