EE-612 / 4 credits

Teacher(s): Anjos André, Canévet Olivier, Marcel Sébastien

Language: English

Remark: Next time: Spring 2025


Every 2 years


This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python source code) to PhD students for their work.



Pattern Recognition, Machine Learning, Linear models, PCA, LDA, MLP, SVM, GMM, HMM.

Learning Prerequisites

Recommended courses

Linear algebra, Probabilities and Statistics, Signal Processing, Python (for the Labs).

Assessment methods

Laboratory and oral exam.

In the programs

  • Number of places: 24
  • Exam form: Multiple (session free)
  • Subject examined: Fundamentals in statistical pattern recognition
  • Lecture: 36 Hour(s)
  • Practical work: 20 Hour(s)

Reference week

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