Universitšt Bonn: Autonomous Intelligent SystemsInstitute for Computer Science VI: Autonomous Intelligent Systems

Technical Neural Networks (L2E4) (MA-INF 4204)

Dr. Nils Goerke

Mondays 12ct - 13:45


NEW Lecture Hall, Meckenheimer Alle 176, Lecture Hall IV, (2nd floor)

Starting Monday 21.Oct 2019, the lecture will be in a NEW LOCATION.
Lecture Hall IV, 2nd floor in Building Meckenheimer Allee 176, (Geozentrum University Bonn).

Enter the building from Meckenheimer Allee (blue pathway), where the Bus stop "Botanischer Garten" is. Inside the building turn right, go down the staircase to the inner court, turn left, re-enter the building, and go to the second floor to lecture Hall IV (green cricle).

As an alternative you can enter the inner court from Katzenburgweg (red pathway).

route to Meckenheimer Allee 176 route to lecture hall MA176 HS-IV

TNN-Exercises Homepage

Please notice: There are no prerequisites for Master of Computer Science students for this module in Winter 2019.
The lecture is organized as 2hrs lecture plus 2 hrs exercises per week.
The lecture starts on: Monday, 7 Oct 2019, 12ct, Lecture Hall 5+6, Endenicher Allee 19c.

This lecture is part of the intelligent systems track of the master programme "Computer Science".

Content of the Lecture:

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.

To be admitted to the exam, you need a minimum of 50% of the points from the paper and pencil exercises and two successful presentations of your solutions within the exercise group.

Universitšt Bonn, Institute for Computer Science, Departments: I, II, III, IV, V, VI