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

Dr. Nils Goerke

Mondays 10 - 12

Grosser Hoersaal

Wegelerstr. 10

Starting: 7.Oct 2024, 10:00. Announcements and lecture material will be distributed via eCampus.
Link to eCampus

Please notice: There are no prerequisites for Master of Computer Science students for this module in Winter 2024.
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".

Location:

Grosser Hoersaal, Wegelerstr 10
A description how to get there is on the eCampus page Link to eCampus

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 three or four 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. The assignments (min 50%) and the exercises (min 2 presentations) are mandatory to be admitted to the exam. So, it is a good idea to actively participate in the weekly exercise groups.