EE-612 / 4 crédits

Enseignant(s): Marcel Sébastien, Canévet Olivier, Anjos André, De Freitas Pereira Tiago

Langue: Anglais

Remark: Registration closed


Frequency

Every 2 years

Summary

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.

Content

Keywords

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.

Dans les plans d'études

  • Forme de l'examen: Multiple (session libre)
  • Matière examinée: Fundamentals in statistical pattern recognition
  • Cours: 36 Heure(s)
  • TP: 20 Heure(s)

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22