# Coursebooks

## Introduction to machine learning

Fua Pascal
Salzmann Mathieu

English

#### Remarque

Le cours du semestre d'automne est donné par M. Salzmann et celui du printemps par M. Fua

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

#### Keywords

Machine learning, classification, regression, algorithms

#### Learning Prerequisites

##### Recommended courses

• Analyse I, II, III
• 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 leraning problems: regression, classification, clustering, dimensionality reduction
• Explain the main differences between them
• Implement algorithms for these machine learning models
• Optimize the main trade-offs such as overfitting, and computational cost vs accuracy
• Implement machine learning methods to real-world problems, and rigorously evaluate their performance using cross-validation. Experience common pitfalls and how to overcome them.

• Lectures
• Lab sessions

#### Expected student activities

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

#### Assessment methods

• Continuous control (graded labs)
• Written final exam

#### Supervision

 Office hours Yes Assistants Yes Forum Yes Others Course website

### Reference week

MoTuWeThFr
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

Lecture
Exercise, TP
Project, other

### legend

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