FTML Project

This repository contains math and code for the Fondamentals of Machine Learning course at EPITA.

Each folder is a section of the project's content.

Table of Contents

• Sections
• Configuration

Sections

1. Bayes estimator and Bayes risk
2. Bayes risk with absolute loss
3. Expected value of empirical risk for OLS
4. Regression on a given dataset
5. Classification on a given dataset
6. Application of supervised learning
7. Application of unsupervised learning

Configuration

Python Environment

There are two ways to set up the Python environment for this project: using pip or conda.

Using Conda

Set up the environment using Conda:

conda env create -f conda_env.yaml

Export the environment:

conda env export --name FTML > conda_env.yaml

Using Pip

Set up the environment using Pip:

pip install -r requirements.txt

Export the environment:

pip freeze > requirements.txt