Coursebooks

Statistical Sequence Processing

EE-605

Lecturer(s) :

Bourlard Hervé

Language:

English

Frequency

Every 2 years

Remarque

Every 2 years. Postponed to next spring 2020.

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

  • Electrical Engineering (edoc), 2018-2019
    • Semester
    • Exam form
      During the semester
    • Credits
      4
    • Subject examined
      Statistical Sequence Processing
    • Number of places
      20
    • Lecture
      28 Hour(s)
    • Practical work
      28 Hour(s)

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