EE-605 / 4 credits

Teacher: Bourlard Hervé

Language: English

Remark: Next time: Spring 2022


Frequency

Every 2 years

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

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.

In the programs

  • Number of places: 20
  • Exam form: During the semester (session free)
  • Subject examined: Statistical Sequence Processing
  • Lecture: 28 Hour(s)
  • Practical work: 28 Hour(s)

Reference week