Additional Resources and Reference Materials
-
Machine Learning (and related topics)
- Math for ML by Iain Murray. Probability, linear algebra, and derivatives.
- Gaussian Processes for Machine Learning, Carl Rasmussen and Christopher Williams.
-
Linear Algebra and Matrix Analysis
- Linear Algebra Review and Reference by Zico Kolter and Chuong Do (free). Light refresher for linear algebra and matrix calculus if you're a bit rusty.
- The Matrix Cookbook by Petersen and Pedersen. Useful matrix identities and derivatives.
- 3Blue1Brown videos provide a gentle and intuitive overview of linear algebra.
-
Probability and Statistics
-
CSE 312 course materials
- Probability and Statistics with Applications to Computing by Alex Tsun (free online).
- Section notes from Anna Karlin's 18au offering of 312: Counting, Combinatorics + intro probability, Conditional probability, Random variables & linearity of expectation, Variance and discrete random variables, Conditional expectation, Joint distributions, Continuous random variables, CLT, tail bounds, and MLE.
- A series of short video lessons from 5MinuteAI.
- Probability Review by Arian Maleki and Tom Do (from Andrew Ng's machine learning class).
- All of Statistics, Larry Wasserman. Chapters 1–5 are a great probability refresher, and the book is a good reference for statistics.
- A First Course in Probability, Sheldon Ross. Elementary concepts (previous editions are a couple bucks on Amazon).
-
CSE 312 course materials
-
Python
- www.learnpython.org — "Whether you are an experienced programmer or not, this website is intended for everyone who wishes to learn the Python programming language."
- Python tutorial
- NumPy tutorial
- Matplotlib PyPlot tutorial
- NumPy for Matlab users
-
LaTeX
- Learn LaTeX in 30 minutes
- Overleaf. An online LaTeX editor.
- LaTeX math symbols
- Detexify — LaTeX handwritten symbol recognition.