Typically, Computational Intelligence is used as an umbrella term for the fields of artificial neural nets (ANN), fuzzy systems (FS) and evolutionary computation (EC). This course offers a thorough introduction into all three fields.
Foundations of ANN: McCulloch-Pitts nets, perceptrons, RBF nets, Hopfield nets, convolutional neural nets (CNN), recurrent nets.
Foundations of FS: Fuzzy sets, fuzzy numbers, fuzzy logic, fuzzy reasoning, fuzzy control, fuzzy clustering.
Foundations of EC: Algorithmic basics, parameterization, analysis methods, limits of deployment.
Students should get an overview about the different aspects of Computational Intelligence. They should be familiar with the essential elements in all three fields (ANN, FS, EC) and able to deploy and adapt these methods for real applications. Moreover, they should be able to assess when to deploy these methods and when not.
Foundations of ANN: McCulloch-Pitts nets, perceptrons, RBF nets, Hopfield nets, convolutional neural nets (CNN), recurrent nets.
Foundations of FS: Fuzzy sets, fuzzy numbers, fuzzy logic, fuzzy reasoning, fuzzy control, fuzzy clustering.
Foundations of EC: Algorithmic basics, parameterization, analysis methods, limits of deployment.
Students should get an overview about the different aspects of Computational Intelligence. They should be familiar with the essential elements in all three fields (ANN, FS, EC) and able to deploy and adapt these methods for real applications. Moreover, they should be able to assess when to deploy these methods and when not.
- Lehrende:r: Nicolas Fischöder
- Lehrende:r: Fabian Ostermann
- Lehrende:r: Andreas Roth
- Lehrende:r: Günter Rudolph