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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

  • 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

Note: Please remove the textbook at checkout using the appropriate discounts if you do not wish to purchase it.

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
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To Register Click on "Add to Cart"
Section Title
Introduction to Linear Algebra With Applications in Statistics, Part 1
Type
Online Asynchronous
Dates
Aug 31, 2022 to Oct 18, 2022
Fee Includes All Costs
Eligible Discounts Can Be Applied at Checkout credit (2 Credits) $885.00
Available for Academic Credit
2 Credit(s)
Required fields are indicated by .