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Theory, programming and simulation of neural networks

Lecturer
Prof. Dr. Ruedi Stoop
Time

Spring Semester

Mittwoch 7:45-9:30 Vorlesung
Mittwoch 9:45-10:30 Übungen

Place ETH Hönggerberg, HIT

Description:

Neuronal networks are an important subset of the methods of artificial intelligence. These methods have become increasingly important in the fields that with the more traditional methods of informatics are difficult to tackle, and therefore have been reserved for human intelligence. In addition to being able to replace and to support a human workforce, these methods also provide insight into the structure and methods of human reasoning.
The lectures are organized as follows. Introductory topics are:

  • graphical methods and game theory (backtracking and constraint propagation)
  • analytical optimization (multidimensional extremal problems, Lagrange multipliers, equilibria, gradient descent)

 

Focus topics are:

  • neuronal networks (biological networks, close-to-biology modeling, spinsystem analogies)
  • evolutionary optimization (genetic algorithms and programming)
  • expert systems (clustering techniques)