Linear Algebra for Data Science

Stat 89A with Michael W. Mahoney, Spring '20.



Week 1

Welcome to Stat 89A!

Jan 20 - Jan 26

  • Welcome to Stat 89A!
  • The course is an introduction to linear algebra for data science. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how probability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc.

Week 1

Welcome to Stat 89A!

Jan 20 - Jan 27

  • The course is an introduction to linear algebra for data science. The course will cover introductory topics in linear algebra, starting with the basics; discrete probability and how probability can be used to understand high-dimensional vector spaces; matrices and graphs as popular mathematical structures with which to model data (e.g., as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.); and geometric approaches to eigendecompositions, least-squares, principal components analysis, etc.

Instructor:

Michael W. Mahoney

Lectures:
  • TTh 12.30 pm - 2.00 pm, 60 Evans Hall

Email:

mmahoney AT stat DOT berkeley DOT edu