Please notice: There are no prerequisites for Master of Computer Science students for this module in Winter 2014.
The lecture is organised as 2hrs lecture plus 2 hrs exercises per week in the time from Tuesday 7 Oct 2014 to Fr 6 Feb 2015.
The lecture starts on:Tuesday, 7 Oct 2014, 8:15 o'clock, Lecture Hall LBH, III.03a .
This lecture is part of the intelligent systems track of the master programme "Computer Science".
The lecture gives an overview over the most important technical neural networks and neural paradigms.
The following topics will be explained in detail: Perceptron, multi-layer perceptron (MLP), radial-basis function nets (RBF), Hopfield nets, self organizing feature maps (SOMS, Kohonen), adaptive resonance theory (ART), learning vector quantization, recurrent networks, back-propagation of error, reinforcement learning, Q-learning, support vector machines (SVM), Neocognitron.
In addition exemplary applications of neural nets will be presented and discussed: function approximation, prediction, quality control, image processing, speech processing, action planning, control of technical processes and robots.
Implementation of neural networks in hardware and software: tools, simulators, analog and digital neural hardware.