Coursebooks 2018-2019

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Statistical Sequence Processing

EE-605

Lecturer(s) :

Bourlard Hervé

Language:

English

Frequency

Every 2 years

Remarque

Every 2 years. Next time: Spring 2019

Summary

This course discusses advanced methods extensively used for the processing, prediction, and classification of temporal (multi-dimensional and multi-channel) sequences. In this context, it also describes key links between signal processing, linear algebra, statistics and artificial neural networks.

Content

Note

Course notes (and relevant book chapters) available.

Keywords

Statistical modeling, Markov models, hidden Markov models, artificial neural networks for sequence processing.

Learning Prerequisites

Recommended courses

Undergraduate level statistics, linear algebra (matric computations, up to PCA) and minimum knowledge/interest in signal processing and machine learning. Programming in Matlab or similar.

Assessment methods

Multiple.

Resources

Websites

In the programs

Reference week

 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German