Machine Learning Notes is a collection of formulas with minimal explanation so it's not suitable for studying, but it's useful as a reference of the most important formulas. It also includes links to videos that provide more elaborate explanations.
- Probability Theory
- Bayesian Decision Theory
- Parameter Estimation
- Linear Regression
- Neural Networks
More topics will be added soon.
Download the PDF file from the latest release.
XeLaTeX is required.
You can compile to PDF by running:
xelatex main.texReporting any mistakes you notice (typos, math errors, or inaccurate explanations) by opening issues, is one of the best ways to contribute.
Fixes, improvements, and additions on the covered topics are welcome.
For new topics, open an issue first.
- Pattern Recognition, S. Theodoridis, K. Koutroumbas
- Pattern Classification, R. Duda, P. Hart, D. Stork
- Pattern Recognition and Machine Learning, C. Bishop
- Machine Learning: A Bayesian and Optimization Perspective, S. Theodoridis
Machine Learning Notes © 2024 by Charalampos Mitsakis is licensed under the CC BY-SA 4.0.