# Coursebooks

## Introduction to machine learning

Fua Pascal

English

#### Withdrawal

It is not allowed to withdraw from this subject after the registration deadline.

#### Remarque

only for IC students

#### 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

• Basic regression : linear models, overfitting, linear regression, ridge regression. SGD for training.
• Supervised classification : logistic regression, linear SVMs and Kernel SVMs.
• Unsupervised classification : k-means clustering, k-NN.
• Dimensionality reduction : PCA and LDA.
• Perceptrons and basic neural networks.
• Simple applications.

#### Keywords

Machine learning, classification, regression, algorithms

#### Learning Prerequisites

##### Recommended courses

• Analyse I, II, III
• Linear algebra

##### Important concepts to start the course

• Basic discrete probability.
• Basic linear algebra (matrix/vector multiplications, systems of linear equations, SVD).
• Multivariate calculus (derivative w.r.t. vector and matrix variables).
• Cost-functions and optimization.
• 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.

• Lecutres
• Lab sessions

#### Expected student activities

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

#### Assessment methods

• Written final exam

#### Supervision

 Office hours Yes Assistants Yes Forum Yes Others Course website

### Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17SG1
17-18INF1
INJ218
INM10
INM200
INM202

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