phy731 - Compulsory Optional Subject Theory (Complete module description)

phy731 - Compulsory Optional Subject Theory (Complete module description)

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Module label Compulsory Optional Subject Theory
Modulkürzel phy731
Credit points 6.0 KP
Workload 180 h
(
Präsenzzeit: 56 Stunden Selbststudium:124 Stunden
)
Institute directory Institute of Physics
Verwendbarkeit des Moduls
  • Master's Programme Physics, Engineering and Medicine (Master) > Mastermodule
Zuständige Personen
  • Doclo, Simon (module responsibility)
  • Anemüller, Jörn (Prüfungsberechtigt)
  • Doclo, Simon (Prüfungsberechtigt)
  • Hohmann, Volker (Prüfungsberechtigt)
  • Kollmeier, Birger (Prüfungsberechtigt)
  • Lücke, Jörg (Prüfungsberechtigt)
  • Meyer, Bernd (Prüfungsberechtigt)
Prerequisites
Bachelor in Physik, Technik und Medizin oder entsprechender Abschluss
Skills to be acquired in this module
Die Studierenden erwerben die theoretischen Voraussetzungen für die numerische und analytische Modellierung komplexer Vorgänge in der Medizin, Biologie und Biophysik, und wenden Forschungsmethoden des Exzellenzcluster Hearing4all im Modellierungsbereich an. Spezielle Kompetenzen abhängig von der gewählten Veranstaltung.
Module contents
Digital Signal Processing Grundlagen der diskreten und integralen Signalrepräsentation (Eigenfunktionen), Abtastung, Signaltransformationen (Fourier-Transformation, Diskrete Fourier-Transformation, FFT, z-Transformation), Systemeigenschaften (Linearität, Zeitinvarianz, Stabilität, Kausalität), Methoden zur Beschreibung und Analyse von digitalen Systemen im Zeit- und Frequenzbereich (Impulsantwort, Übertragungsfunktion), stochastische Prozesse und lineare Systeme, digitale Filter, Optimalfilter, Adaptive Filter im Zeit- und Frequenzbereich.
Machine Learning II - Advanced Learning and Inference: This course builds up on the basic models and methods introduced in introductory Machine Learning lectures. Advanced Machine Learning models will be introduced alongside methods for efficient parameter optimization. Analytical approximations for computationally intractable models will be defined and discussed as well as stochastic (Monte Carlo) approximations. Advantages of different approximations will be contrasted with their potential disadvantages. Advanced models in the lecture will include models for clustering, classification, recognition, denoising, compression, dimensionality reduction, deep learning, tracking etc. Typical application domains will be general pattern recognition, computational neuroscience and sensory data models including computer hearing and computer vision. Processing and analysis of biomedical data Normal distributions and significance testing, Monte-Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confidence intervals, Dipole source analysis, Analysis of variance. Each technique is explained, tested and discussed in the exercises.
Literaturempfehlungen
- B. Girod, R. Rabenstein, A. Stenger, Signals and Systems, Wiley, 2001.
- J. G. Proakis, D. G. Manolakis, Digital Signal Processing – Principles, Algorithms and Applications, Prentice
   Hall, 2007.
-  A. V. Oppenheim, R. W. Schafer, Discrete-Time Signal Processing, Prentice Hall, 2009.
- S. Haykin, Adaptive Filter Theory, Prentice Hall, 2001.
- C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2006 (best suited for lecture).
- K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
- D. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003
  (free online)
- K. Petersen, M. Pederson, The Matrix Cookbook, (free online) 
- Kirkwood B.R. and Sterne A.C., Essential Medical Statistics: 2nd editition. Blackwell Science. Oxford,
   2003
- Cho, Z.H. and Singh J. P. J. M.: Foundations of Medical Imaging. John Wiley, New York, 1993
- Kutz, J.N. Data-Driven Modeling and Scientific Computation: Methods for complex systems and Big Data.
  Oxford University Press, Oxford, 2013
Links
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency Sommersemester
Module capacity unlimited
Type of module Wahlpflicht / Elective
Module level MM (Mastermodul / Master module)
Teaching/Learning method  Digital Signal Processing: Vorlesung: 2 SWS, Übungen: 2 SWS
 Machine Learning II – Advanced Learning and Inference Methods: Vorlesung: 2 SWS, Übungen: 2 SWS
 Processing and analysis of biomedical data: Vorlesung: 2 SWS, Übung: 2 SWS
Lehrveranstaltungsform Comment SWS Frequency Workload of compulsory attendance
Lecture 2 WiSe 28
Seminar SoSe oder WiSe 0
Exercises 2 WiSe 28
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
M