ml_nutshell

This repository contains python implementation codes of most common machine learning models and fundamental mathematical concepts behind them.

Content

1. 6_risks.pdf $\rightarrow$ The theory behind Empirical Risk and True Risk and the relation between them
2. 6_risks_2.pdf $\rightarrow$ Further theory on Loss Function and Risks.

References

1. Lecture notes from the course 'Machine Learning Theory' taken by Prof. Ruth Urner (Winter Semester 2016/17, Universität Tübingen)
2. Lecture notes from the course 'Advanced Introduction to Machine Learning' (CMU-10715) taken by Prof. Dr. Barnabás Póczos (Carnegie Mellon University)

Books

1. Learning Theory from First Principles by Francis Bach
2. Hands-on ML with Scikit-Learn, Keras & TensorFlow by Aurélien Géron
3. Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong