Materials
References
There is no required textbook for this course. However, most lectures are accompanied by optional readings. While the lectures do not follow any single book, these references can deepen your understanding and offer additional perspectives.
The recommended readings are drawn from the following books:
Probabilistic Machine Learning: An Introduction (PML)
Our main textbook reference, this book offers a broad and accessible introduction to machine learning through probabilistic modeling. It blends theory with practical examples, making it a useful guide throughout the course.
Pattern Recognition and Machine Learning (PRML)
A comprehensive and mathematically rigorous textbook that introduces machine learning through a probabilistic perspective. This will be one of our main references, providing solid foundations in models, inference, and learning principles.
Understanding Machine Learning: From Theory to Algorithms (UML)
A more theory-oriented book that covers a wide range of topics in machine learning. While we won't cover it in depth, it is a good reference for students who want to dive deeper into the theory behind the algorithms.
Resources for Math Background
Math4ML Textbook
- Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Probability
- Probability Cheatsheet, Blitzstein & Chen
- Probability Review (slides), Rob Hall
Linear Algebra
- Linear Algebra Review (notes), Zico Kolter
- Linear Algebra Review (videos), Zico Kolter
- Matrix Cookbook (notes), Petersen & Pederson
- Matrix Derivatives Cheatsheet, Kirsty McNaught
Geometry:
- point distance to plane (Khan Academy)
- visualizing dot product (Khan Academy)
- define a line with a vector (Khan Academy)
Programming Resources
- Python for Everybody, Charles Severance
- Python for Data Science Handbook, Jake VanderPlas