Machine Learning Course

This repository contains the material for my Machine Learning course: lecture notes, code examples and lab works. I have tried to write them in such a way that they should be accessible to anyone wanting to learn the subject, regardless of whether you are one of my students or not.

Table of Contents

• About Artificial Intelligence [ notes ]
• Machine Learning: an introduction [ notes ]

Supervised Learning

Supervised Learning is a subset of Machine Learning in which expected results are fed into the system alongside training data.

Fundamentals

• Principles of supervised learning [ notes ]
• End-to-end project workflow [ notes | example ]
• Assessing classification performance [ notes | example ]
• Learning via Gradient Descent [ notes ]
• 👨🏻‍💻 Essential tools [ lab ]
• 👩🏻‍💻 Predict heart disease [ lab | solution ]

Algorithms

Update in progress!

• 🚧 Linear Regression [ notes ]
• 🚧 Decision Trees & Random Forests [ notes ]
• ... (more to come)

Neural Networks and Deep Learning

Update in progress!

Deep Learning is a subset of Machine Learning based on the usage of large neural networks trained on vast amounts of data.

• Feedforward Neural Networks [ notes | example ]
• 👩🏻‍💻 Introduction to PyTorch [ lab | examples ]
• 👨🏻‍💻 Classify 2D data with a neural network [ lab | solution ]
• Convolutional Neural Networks [ notes | example ]
• ... (more to come)

Reinforcement Learning

Update in progress!

Reinforcement Learning is a subset of Machine Learning concerned with the maximization of rewards in a dynamic environment.

• 🚧 Introduction to Reinforcement Learning [ notes ]
• ... (more to come)

Usage

git clone https://github.com/bpesquet/mlcourse.git
cd mlcourse
python {path to Python code file}

Development notes

Toolchain

This project is built with the following software:

• Poetry for dependency management;
• Black for code formatting;
• Pylint to detect mistakes in the code;
• pytest for testing the code;
• Marp for showcasing notes as slideshows during lectures.

Useful commands

# Reformat all Python files
black mlcourse

# Check the code for mistakes
pylint mlcourse

# Run all code examples as unit tests
# The -s flag prints code output
pytest [-s] mlcourse

License

Creative Commons for textual content and MIT for code.

Copyright © 2024-present Baptiste Pesquet.