Course Description

This is a first course in linear algebra, aimed at students with diverse backgrounds. It covers the content of a standard textbook: linear systems, vectors and matrices, dimensions and bases of vector spaces, eigenvalues and eigenvectors, and singular value decomposition. It is also dedicated to explaining applications of these linear algebra concepts in classic analysis methods as well as state-of-the-art statistical inference and machine learning approaches. In the application portion of the course, we will strive to tailor the content to the interests and research needs of the students.

This is the second part of a two-part course. The completion of the first part (MATH 215) OR an equivalent course OR the knowledge of vectors, matrix operations, orthogonality, and determinants is highly recommended before taking the second part. Registration is required separately for each part of the course.

Learner Outcomes

• Understand systems linear equations and their matrix representation
• Learn the concept of vector spaces, subspaces, and linear dependence
• Learn spectral methods for analyzing matrices
• Understand statistical methods based on linear models

Credentials

This course applies toward the Bioinformatics Curiosity digital badge.

Textbook Information

Textbook Required:

Introduction to Linear Algebra (5th Edition, 2016) 
by Gilbert Strang
ISBN: 9780980232776 |  $110.00

Notes:

•  If you have already purchased the textbook for MATH 215, do not purchase an additional textbook for MATH 216.
• Please remove the textbook at checkout using the appropriate discounts if you do not wish to purchase it.