This is a survey course on numerical methods prominent in modern data analysis and machine learning. Building on basic methods of optimization and numerical linear algebra, the course will explore the role of numerical methods for treating several classes of data analysis problems, including low rank factorizations and completion of matrix data; function approximation by kernel methods; and analysis of data on graphs. The course is organized around four one-week units:
- Optimization basics
- Matrix data and latent factor models
- Kernel interpolation and Gaussian processes
- Numerical methods for graph data analysis
Students should know basic programming and should have prior courses on linear algebra and multivariable calculus. Familiarity with numerical methods or machine learning will be useful, but not required.
There is no textbook, but course notes will be posted on the web site prior to each lecture.
The course will be graded on the basis of homeworks of one to two problems that are posted each lecture and are due one week later. Students should also read the course notes before each lecture.