Παρουσίαση/Προβολή

(TP234) -  Papadourakis G.

Περιγραφή Μαθήματος

The course introduces the theory and practice of neural computation. It provides the principles of neurocomputing with artificial neural networks widely used for addressing real-world problems such as classification, regression, system identification, pattern recognition, data mining, time-series prediction, etc. Artificial neural network models are inspired by biological neural networks. The course begins with an overview of information processing principles in biological systems. The core of the course consists of the theory and properties of major neural network algorithms and architectures. Two main topics are covered: learning linear models by perceptrons, and learning non-linear models by probabilistic neural networks, multilayer perceptrons, radial-basis function networks, and Kohonen neural networks. The students will have a chance to implement and try out several of these models on practical problems. By the end of the course, students will be able to assess the applicability of neural networks for a given task, select an appropriate neural network paradigm, and build a working neural network model for the task.

Ημερομηνία δημιουργίας

Δευτέρα 9 Μαΐου 2011