Coursebooks

Fundamentals in statistical pattern recognition

EE-612

Lecturer(s) :

Anjos André
Canévet Olivier
De Freitas Pereira Tiago
Marcel Sébastien

Language:

English

Frequency

Every 2 years

Remark

Next time: Spring 2021

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

This course will cover the broad regression, classification and probability distribution modeling methods and more particularly: Linear regression, Logistic regression, k-NN, Decision Trees, Boosting, Dimensionality reduction (PCA, LDA, t-SNE), k-Means, GMMs, MLPs, CNNs, SVMs.

 

A - Introduction

 

B - Regression and Classification

 

C - Dimensionality reduction and Clustering

 

D - Probability distribution modelling

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.

Resources

Websites

In the programs

    • Semester
    • Exam form
       Multiple
    • Credits
      4
    • Subject examined
      Fundamentals in statistical pattern recognition
    • Number of places
      30
    • Lecture
      36 Hour(s)
    • Practical work
      20 Hour(s)

Reference week

 
      Lecture
      Exercise, TP
      Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German