Loading...

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, 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 first part of a two-part course. Registration is required separately for each part of the course.

Learner Outcomes

When you complete the course successfully, you will be able to:

  • 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

Microcredential(s):

This course applies toward the Bioinformatics Endeavor digital badge.

 

Textbook Information

A textbook is required or recommended for this course. Click here to view a textbook list for FAES courses and purchasing information. Please note that tuition does not include textbooks.

Prerequisites

One semester of analytic geometry or calculus is recommended, but not required. Basic knowledge of vectors, cartesian coordinates, and algebra is required. If you are unsure that you meet the prerequisite requirements, please contact registrar@faes.org and provide information about your course of interest and background knowledge

REFUND
Follow the link to review FAES Tuition Refund Policy.

Loading...
Thank you for your interest in this course. Unfortunately, the course you have selected is currently not open for enrollment. Please complete a Course Inquiry so that we may promptly notify you when enrollment opens.
Required fields are indicated by .