Please notice: There are no prerequisites for Master of Computer Science students for this module in Winter 2017.
The lecture is organized as 2hrs lecture plus 2 hrs exercises per week.
The lecture starts on:Monday, 9 Oct 2017, 10 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, Convolutional Neural Networks, Deep Learning.
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.
The exercises are arranged to intensify the work with
the research topics presented in the lecture.
You will get weekly paper-and-pencil assignments that are designed to
be worked on in two person groups and completed within one week.
Your results of the assignments shall be presented and discussed during
the exercise group to practice and improve your oral presentation skills.
The paper and pencil assignments are accompanied by small programming
tasks to be completed using individually implemented programms and
stat of the art simulation tools.
You will need to reach half of the possible points from the paper and pencil assignments to be admitted to the examination.