# Fiches de cours

## Introduction to machine learning (BA3)

Salzmann Mathieu

English

#### Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

#### Content

• Introduction: General concepts, data representation, basic optimization.
• Linear methods: Linear regression, least-square classification, logistic regression, linear SVMs.
• Nonlinear methods: Polynomial regression, kernel methods, K nearest neighbors
• Deep learning: Multi-layer perceptron, CNNs.
• Unsupervised learning: Dimensionality reduction, clustering.

#### Keywords

Machine learning, classification, regression, algorithms

#### Learning Prerequisites

Linear algebra

##### Important concepts to start the course

• Basic linear algebra (matrix/vector multiplications, systems of linear equations, SVD).
• Multivariate calculus (derivative w.r.t. vector and matrix variables).
• Basic programming skills (labs will use Python).

#### Learning Outcomes

By the end of the course, the student must be able to:
• Define the following basic machine learning problems: regression, classification, clustering, dimensionality reduction
• Explain the main differences between them
• Derive the formulation of these machine learning models
• Assess / Evaluate the main trade-offs such as overfitting, and computational cost vs accuracy
• Implement machine learning methods on real-world problems, and rigorously evaluate their performance using cross-validation.

• Lectures
• Lab sessions

#### Expected student activities

• Attend lectures
• Attend lab sessions and work on the weekly theory and coding exercises

#### Assessment methods

• Two graded exercise sessions (10% each). Can be done remotely with a zoom presence to answer questions
• Final exam (80%)

#### Supervision

 Office hours No Assistants Yes Forum Yes Others Course website

### Semaine de référence

LuMaMeJeVe
8-9 CM2  INJ218
INM202
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

Cours
Exercice, TP
Projet, autre

### légende

• Semestre d'automne
• Session d'hiver
• Semestre de printemps
• Session d'été
• Cours en français
• Cours en anglais
• Cours en allemand