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

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

Dr. Nils Goerke

Mondays 10ct - 11:45

via Internet

Starting Monday 2.Nov 2020, the lecture will be opoerated via the online tool Zoom. Announcements and lecture material will be distributed via eCampus.

TNN-Exercises Homepage


Please notice: There are no prerequisites for Master of Computer Science students for this module in Winter 2020.
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".



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.


Exercises:

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 simulation tools.

To be prepared for the examination you should keep track of the lecture content by doing the assignments. Although the exercises and the assignments are not necessary to be admitted to the exam it is a good idea to actively participate in the weekly exercise groups.



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