Principles of Neural Networks (GNN)

Understand How AI Really Works: Develop your own neural networks, understand their architecture, and analyze how they learn and make decisions.

Contents

• Neural models
• Network topologies
• Learning rules
• Recurrent networks
• Dynamical systems

Competencies

Students are able to

• implement neural models,
• classify neural topologies
• understand and apply learning rules,
• understand simple recurrent neural networks,
• analyse a network as a dynamical system

Literature

• Fischer, Jörn: Maschinelles Lernen für Dummies, Wiley CH, ISBN: 978-3527720552, 2024
• I. Goodfellow, Y.Bengio, A. Courville : Deep Learning, MIT Press, ISBN: 978-0262035613, 2016
• Mitchell, Tom: Machine Learning. McGraw-Hill, 1997
• Zell, Andreas: Simulation Neuronaler Netze. Oldenbourg Verlag, München, 1997

Lecturer

• Prof. Dr. Jörn Fischer
• Prof. Dr. Ivo Wolf

Recommended Previous Knowledge 

• Math for Computer Scientists 1 (MA1)
• Math for Computer Scientists 2 (MA2)
• Maschinelles Lernen (MLE) von Vorteil

Module Details

Semester	6/7
Lecture Language	German
Frequency	Summer Term
Credit Points (ECTS)	5
Course Coordinator	Prof. Dr. Jörn Fischer
Duration	1 Semester
Course Achievement	None
Prerequisite for exam	Project (PA)
Exam	Written Exam 60 Minutes (K60)

Weekly Hours (SWS) 

Lecture	2 SWS
Exercises	2 SWS
Sum	4 SWS

Work Load 

Lecture	30 h
Self Study	90 h
Assignments	30 h
Sum	150 h