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Module guide

Modul CS4405-KP04, CS4405

Neuroinformatics (NeuroInf)

Duration:


1 Semester
Turnus of offer:


each summer semester
Credit points:


4
Course of studies, specific field and terms:
  • Master CLS 2023 (compulsory), computer science, 2nd semester
  • Master Auditory Technology 2022 (optional subject), Auditory Technology, 2nd semester
  • Master Auditory Technology 2017 (optional subject), Auditory Technology, 2nd semester
  • Master MES 2020 (optional subject), computer science / electrical engineering, Arbitrary semester
  • Master CLS 2016 (compulsory), computer science, 2nd semester
  • Master Robotics and Autonomous Systems 2019 (optional subject), Elective, 1st or 2nd semester
  • Master MES 2014 (optional subject), computer science / electrical engineering, Arbitrary semester
  • Master MES 2011 (optional subject), mathematics, 2nd semester
  • Bachelor MES 2011 (optional subject), optional subject medical engineering science, 6th semester
  • Master Computer Science 2012 (optional subject), advanced curriculum organic computing, 2nd or 3rd semester
  • Master MES 2011 (advanced curriculum), imaging systems, signal and image processing, 2nd semester
  • Master Computer Science 2012 (optional subject), advanced curriculum intelligent embedded systems, 2nd or 3rd semester
  • Master Computer Science 2012 (compulsory), specialization field robotics and automation, 2nd semester
  • Master Computer Science 2012 (compulsory), specialization field bioinformatics, 2nd semester
  • Master CLS 2010 (compulsory), computer science, 2nd semester
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:
Teachers:
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:

Admission requirements for taking the module:
- None

Admission requirements for participation in module examination(s):
- Successful completion of exercises as specified at the beginning of the semester

Module Exam(s):
- CS4405-L1: Neuroinformatics, written exam, 90 min, 100% of module grade

According to the old version of the MES Bachelor Examination Regulations (until WS 2011/2012), an elective subject is scheduled for the 4th semester instead of the 6th semester.

Letzte Änderung:
1.2.2022