# Coursebooks

## Fundamentals in statistical pattern recognition

#### Lecturer(s) :

Anjos André
Canévet Olivier
De Freitas Pereira Tiago
Marcel Sébastien

English

Every 2 years

#### Remark

Registration closed

#### 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

• Data representation,
• Pattern Recognition and Machine Learning,
• Lab preparation (JupyterHub, Python and pyTorch).

B - Regression and Classification

• Linear Regression,
• Logistic Regression and Regularization, Overfitting and Capacity,
• k-NN, Decision Trees,
• Artificial Neural Networks: Multi-Layer Perceptron (MLP) and Back-Propagation
• Deep Learning : Convolutional Neural Networks (CNN) and Optimization
• Support Vector Machines

C - Dimensionality reduction and Clustering

• Principal Component Analysis (PCA),
• Linear Discriminant Analysis (LDA),
• k-Means, Single Linkage,
• t-SNE.

D - Probability distribution modelling

• Gaussian Mixture Models (GMM) and the Expectation-Maximization (EM).

#### 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

• Semester
• Exam form
Multiple
• Credits
4
• Subject examined
Fundamentals in statistical pattern recognition
• Number of places
24
• 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