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Tags: bayesian models / bayesian networks / gaussian models / generalized linear models / kernel methods / kernels / linear models / linear regression / logistic regression / machine learning / mathematics / optimization / probability / statistics
Note: this “cheat sheet” is more like a machine learning math reference book!
This cheat sheet contains many classical equations and diagrams on machine learning, which will help you quickly recall knowledge and ideas in machine learning.
This cheat sheet has three significant advantages:
- Strong typed. Compared to programming languages, mathematical formulas are weakly typed. For example, X can
be a set, a random variable, or a matrix. This causes difficulty in understanding the meaning of formulas. In this
cheat sheet, I try my best to standardize symbols used, see section §.
- More parentheses. In machine learning, authors are prone to omit parentheses, brackets and braces, this usually
causes ambiguity in mathematical formulas. In this cheat sheet, I use parentheses(brackets and braces) at where
they are needed, to make formulas easy to understand.
- Less thinking jumps. In many books, authors are prone to omit some steps that are trivial in his option. But it often
makes readers get lost in the middle way of derivation.
Recommended Prerequisites: advanced mathematics, basic understanding of machine learning
Go to Content: Machine Learning Cheat Sheet