This repository contains python implementation codes of most common machine learning models and fundamental mathematical concepts behind them.
- 6_risks.pdf
$\rightarrow$ The theory behind Empirical Risk and True Risk and the relation between them - 6_risks_2.pdf
$\rightarrow$ Further theory on Loss Function and Risks.
- Lecture notes from the course 'Machine Learning Theory' taken by Prof. Ruth Urner (Winter Semester 2016/17, Universität Tübingen)
- Lecture notes from the course 'Advanced Introduction to Machine Learning' (CMU-10715) taken by Prof. Dr. Barnabás Póczos (Carnegie Mellon University)
- Learning Theory from First Principles by Francis Bach
- Hands-on ML with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong (Note: This version is free to view and download for personal use only. Not for re-distribution, re-sale, or use in derivative works. ©by M. P. Deisenroth, A. A. Faisal, and C. S. Ong, 2024. https://mml-book.com.)