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

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

Dr. Nils Goerke

Mondays 8:30 - 10:00

Lecture Hall III.03a, Friedrich-Ebert-Allee 144

Lecture starts on Monday, 19 Oct 2015, 10 o'clock, Lecture Hall III.03a

Rest Examination, Fri 7.4.2017, 9:00 - 10:40, Lecture Hall III.03a, LBH

Further Exam Inspection dates:

Friday 31.3.2017, 9:00 - 9:50, LBH, Room I.42

Monday 3.4.2017, 9:00 - 9:50, LBH, Room I.42

Tuesday 4.4.2017, 9:00 - 9:50, LBH, Room I.42

Tuesday 4.4.2017, 15:15 - 16:15, LBH, Room I.42

Please have a valid ID with photo with you.

Exam Results are in BASIS ny now. There was a technical delay. Sorry for that.

TNN-Exercises Homepage

Please notice: There are no prerequisites for Master of Computer Science students for this module in Winter 2016.
The lecture is organized as 2hrs lecture plus 2 hrs exercises per week in the time from Tuesday 25 Oct 2016 to Fr 10 Feb 2017.
The lecture starts on:Monday, 17 Oct 2016, 8 o'clock, Lecture Hall LBH, III.03a .

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 can 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.

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