![]() ![]() This is the only course at Stanford whose syllabus includes nearly all the math background for CS229, which is why CS229 and CS230 specifically recommend it (or other courses resting on it). The widespread use of computers makes it important for users of math to understand concepts: novel users of quantitative tools in the future will be those who understand ideas and how they fit with examples and applications. The course emphasizes computations alongside an intuitive understanding of key ideas. ![]() The multivariable calculus portion includes unconstrained optimization via gradients and Hessians (used for energy minimization), constrained optimization (via Lagrange multipliers, crucial in economics), gradient descent and the multivariable Chain Rule (which underlie many machine learning algorithms, such as backpropagation), and Newton's method (an ingredient in GPS and robotics). We keep the library up-to-date, so you may find new or improved material here over time. Here, you can browse videos, articles, and exercises by topic. The linear algebra portion includes orthogonality, linear independence, matrix algebra, and eigenvalues with applications such as least squares, linear regression, and Markov chains (relevant to population dynamics, molecular chemistry, and PageRank) the singular value decomposition (essential in image compression, topic modeling, and data-intensive work in many fields) is introduced in the final chapter of the e-text. Welcome to the Physics library Physics is the study of matter, motion, energy, and force. ![]() Linear algebra in large dimensions underlies the scientific, data-driven, and computational tasks of the 21st century. This course provides unified coverage of linear algebra and multivariable differential calculus, and the free course e-text connects the material to many fields. ![]()
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