Brief Description of Course Content Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues.

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.

Elementary set theory and solution sets of systems of linear equations. An introduction to proofs and the axiomatic methods through a study of the vector space axioms. Linear analytic geometry. Linear ...

This Guy Learned Linear Algebra in Ten Days, And You Can Too At MIT you can take a ton of science classes online. And, in true MIT fashion, someone just did them all at an extraordinary speed ...

Students should ideally have taken the course Mathematical Methods (MA100) or equivalent, entailing intermediate-level knowledge of linear algebra, linear independence, eigenvalues and diagonalisation ...

Tom Brown, Anthropic’s co-founder and former OpenAI engineer, went from earning a B- in linear algebra to shaping the AI ...

We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general ...