EE-612 / 4 credits

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

Language: English

Remark: Next time: Spring 2023


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.

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