Document Relation to CS 189/289A
This document is explicitly meant to support UC Berkeley’s CS 189/289A by providing the mathematical background needed for an introductory class in machine learning. It states that it is “an attempt to provide a summa...
This document is explicitly meant to support UC Berkeley’s CS 189/289A by providing the mathematical background needed for an introductory class in machine learning. It states that it is “an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning” that at UC Berkeley is known as CS 189/289A [:cite[1]{ln=2}]. It also says it assumes you already know multivariable calculus and linear algebra and that it is not a replacement for the prerequisite classes [:cite[1]{ln=3}], [:cite[1]{ln=4}]. Concretely, its contents align with core topics typically required in CS 189/289A, including: Linear algebra foundations (vector spaces, linear maps, norms/metrics, inner products, projections, eigen related matrix concepts, SVD, etc.) [:cite[3]{ln=1}], [:cite[2]{ln=1}], [:cite[6]{ln=1}], [:cite[4]{ln=1}], [:cite[5]{ln=1}]. Calculus/optimization concepts such as gradients, Hessians, and convexity (listed in the document’s outline/sections) [math4ml.pdf Calculus and Optimization], [math4ml.pdf Convexity]. Probability essentials like conditional probability, Bayes’ rule, expectations/variance, covariance, distributions, and maximum likelihood / MAP (listed in the document’s outline/sections) [math4ml.pdf Probability], [math4ml.pdf Estimation of Parameters], [math4ml.pdf The Gaussian distribution]. So, the relationship is: CS 189/289A uses machine learning methods, and this document supplies the prerequisite math toolkit those methods build on, in a compact “overview + pointers to deeper treatments” form [:cite[1]{ln=2}], [:cite[1]{ln=5}].