Machine Learning
Classes
You can find a web page for each class on the Classes page.
Books and sources
The principal textbook for the course will be An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani. The full text is available from that link together with the accompanying Python library.
Supplementary materials will draw on these books:
- Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
- Python Data Science Handbook Essential Tools for Working with Data by Jake VanderPlas. The full text is available at that link as are the accompanying Jupyter notebooks
- The 2nd Edition of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron. The accompanying series of Jupyter notebooks contain example code for the book.
- Pattern Recognition and Machine Learning by Christopher Bishop
- Parts of the section on ethics are based on a chapter from the book Deep Learning for Coders with fastai and PyTorch by Jeremy Howard & Sylvain Gugger which was coauthored by Rachel Thomas.
- This course places an emphasis on putting Machine Learning algorithms into practice. If you're interested in learning how to program those algorithms from scratch see Data Science From Scratch by Joel Grus and its accompanying code.
Datasets
You can find links to datasets used in the course.