Module CS4405-KP04, CS4405

Duration

1 Semester

Turnus of offer

each summer semester

Credit points

4

Course of studies, specific fields and terms:

• Master CLS 2023, compulsory, computer science
• Master Auditory Technology 2022, optional subject, Auditory Technology
• Master Auditory Technology 2017, optional subject, Auditory Technology
• Master MES 2020, optional subject, computer science / electrical engineering
• Master CLS 2016, compulsory, computer science
• Master Robotics and Autonomous Systems 2019, optional subject, Elective
• Master MES 2014, optional subject, computer science / electrical engineering
• Master MES 2011, optional subject, mathematics
• Bachelor MES 2011, optional subject, optional subject medical engineering science
• Master Computer Science 2012, optional subject, advanced curriculum organic computing
• Master MES 2011, advanced curriculum, imaging systems, signal and image processing
• Master Computer Science 2012, optional subject, advanced curriculum intelligent embedded systems
• Master Computer Science 2012, compulsory, specialization field robotics and automation
• Master Computer Science 2012, compulsory, specialization field bioinformatics
• Master CLS 2010, compulsory, computer science

Classes and lectures:

• Neuroinformatics (lecture, 2 SWS)
• Neuroinformatics (exercise, 1 SWS)

Workload:

• 20 hours exam preparation
• 55 hours private studies
• 45 hours in-classroom work

Contents of teaching:

• The human brain and abstract neuron models
• Learning with a single neuron: * Perceptrons * Max-Margin Classification * LDA and logistic Regression
• Network architectures: * Hopfield-Networks * Multilayer-Perceptrons * Deep Learning
• Unsupervised Learning: * k-means, Neural Gas and SOMs * PCA & ICA * Sparse Coding

Qualification-goals/Competencies:

• The students are able to understand the principle function of a single neuron and the brain as a whole.
• They know abstract neuronal models and they are able to name practical applications for the different variants.
• They are able to derive a learning rule from a given error function.
• They are able to apply (and implement) the proposed learning rules and approaches to solve unknown practical problems.

Grading through:

• Written or oral exam as announced by the examiner

Responsible for this module:

• Prof. Dr. rer. nat. Thomas Martinetz

Teacher:

• Institute for Neuro- and Bioinformatics
• Prof. Dr. rer. nat. Thomas Martinetz
• Prof. Dr. rer. nat. Amir Madany Mamlouk

Literature:

• S. Haykin : Neural Networks London: Prentice Hall, 1999
• J. Hertz, A. Krogh, R. Palmer : Introduction to the Theory of Neural Computation Addison Wesley, 1991
• T. Kohonen : Self-Organizing Maps Berlin: Springer, 1995
• H. Ritter, T. Martinetz, K. Schulten : Neuronale Netze: Eine Einführung in die Neuroinformatik selbstorganisierender Netzwerke Bonn: Addison Wesley, 1991

Language:

• offered only in German

Notes:

Last Updated:

01.02.2022