Starting tbd, the lecture will be opoerated via the online tool
Zoom. Announcements and lecture material will be distributed via
Please notice: There are no prerequisites for Master of Computer
Science students for this module in Winter 2022.
The lecture is organized as 2hrs lecture plus 2 hrs exercises per week.
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 or three 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
To be prepared for the examination you should keep track of the lecture content by doing the assignments. The exercises and the assignments are mandatory to be admitted to the exam. So, it is a good idea to actively participate in the weekly exercise groups.