The project contains notes and proofs as Jupyter notebooks, as well as, Python code implementing algorithms as presented in the book "Pattern Recognition and Machine Learning" by Christopher Bishop.

This program comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to redistribute it under certain conditions; See the GNU General Public License v3 for more details.

In order to run the notebooks locally type the following:

make install
make jupyter

To generate a PDF document from the notebooks just type:

make notes

To generate markdown documents from the notebooks just type:

make markdown

• 1. Introduction
• 2. Probability Distributions
• 3. Linear Models for Regression
• 4. Linear Models for Classification
• 5. Neural Networks
• Gradient Descent Algorithms
• 6. Kernel Methods
• 7. Sparse Kernel Machines
• 9. Mixture Models and EM

• PRML Book
• PRML Errata
• An overview of gradient descent optimization algorithms by Sebastian Ruder
• An updated overview of recent gradient descent algorithms by John Chen
• 3Blue1Brown YouTube Channel