Additional resources and reference materials

• Machine Learning (and related topics)
• Math for ML by Iain Murray. Probability, linear algebra & derivatives.
• Gaussian Processes for Machine Learning, Carl Rasmussen & 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 & 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).
  • A series of short video lessons.
  • Section notes from Anna Karlin's 18au offering of 312: Counting, Combinatorics + intro probability, Conditional probability; Random variables & linearity of expectation, Variance and discrete r.v.s, Conditional expectation, Joint distributions, Continuous random variables, CLT, tail bounds and MLE.
• 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)
• 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
• 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

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