Turnus of offer:
each summer semester
Course of studies, specific field and terms:
- Master Computer Science 2019 (module part), Module part, Arbitrary semester
- Master MES 2020 (module part), computer science / electrical engineering, Arbitrary semester
- Master Entrepreneurship in Digital Technologies 2020 (module part), Module part, Arbitrary semester
- Master Medical Informatics 2019 (module part), Module part, Arbitrary semester
- Master Biophysics 2019 (module part), advanced curriculum, 2nd semester
- Master IT-Security 2019 (module part), Module part, 1st or 2nd semester
- Master Medical Informatics 2014 (module part), Module part, Arbitrary semester
- Master Entrepreneurship in Digital Technologies 2014 (module part), Module part, Arbitrary semester
- Master MES 2014 (module part), computer science / electrical engineering, 2nd semester
- Master Computer Science 2014 (module part), Module part, Arbitrary semester
Classes and lectures:
- NeuroInformatics (exercise, 1 SWS)
- NeuroInformatics (lecture, 2 SWS)
- 45 Hours in-classroom work
- 55 Hours private studies
- 20 Hours exam preparation
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
- Unxupervised Learning: * k-means, Neural Gas and SOMs * PCA & ICA * Sparse Coding
- 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.
- exam type depends on main module
Responsible for this module:
- 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
Examination prerequisites can be defined at the beginning of the semester. If prerequisite courses are defined, they must have been completed and positively evaluated before the first examination.
(Is module part of CS4410, CS4511)
(Is equal to CS4405)
Admission requirements for the module:
Admission requirements for the examination:
- Successful completion of exercises during the semester.
Translated with www.DeepL.com/Translator (free version)
Letzte Änderung: 1.2.2022
für die Ukraine