This course provides unified coverage of linear algebra and multivariable differential calculus. It discusses applications connecting the material to many quantitative fields. Linear algebra in large dimensions underlies the scientific, data-driven, and computational tasks of the 21st century. The linear algebra portion of the course includes orthogonality, linear independence, matrix algebra, and eigenvalues as well as ubiquitious applications: least squares, linear regression, Markov chains (relevant to population dynamics, molecular chemistry, and PageRank), singular value decomposition (essential in image compression, topic modeling, and data-intensive work in the natural sciences), and more. The multivariable calculus material includes unconstrained optimization via gradients and Hessians (used for energy minimization in physics and chemistry), 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 (a crucial part of how GPS works). The course emphasizes computations alongside an intuitive understanding of key ideas, making students well-prepared for further study of mathematics and its applications to other fields. The widespread use of computers makes it more important, not less, for users of math to understand concepts: in all scientific fields, novel users of quantitative tools in the future will be those who understand ideas and how they fit with applications and examples. This is the only course at Stanford whose syllabus includes nearly all the math background for CS 229, which is why CS 229 and CS 230 specifically recommend it (or other courses resting on it). For frequently asked questions about the differences between Math 51 and CME 100, see the FAQ on the placement page on the math department website.
- Intensive Study: Data Science
- requirements: The Math Placement Diagnostic is required to enroll in the course unless Math 21 or 42 have been completed at Stanford. The Math Placement Diagnostic should not be considered a barrier to enrollment, but is a way for the student to prepare for the course and ensure that they have the prerequisite knowledge. A certain score is not required in order to enroll, simply taking the diagnostic is sufficient.
- Online Format: Synchronous - This course is taught in real-time, and students are expected to attend virtual sessions at specific times during the week. For more information on the schedule options for this course, please visit the Stanford Explore Courses website.
MATH 21 or MATH 42 or Math Placement Diagnostic.