MATH 216 - Introduction to Linear Algebra With Applications in Statistics, Part 2

Course Description

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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

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

Demonstrate the calculations of eigenvalues and eigenvectors
Carry out matrix diagonalization and positive definite matrices
Explain the singular value decomposition (SVD) and principal component analysis (PCA)
Apply linear algebra in statistics

Microcredential(s):

This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information

A textbook is available for this course.

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Prerequisites

MATH 215 or an equivalent course OR the knowledge of vectors, matrix operations, orthogonality, and determinants is highly recommended before taking MATH 216. One semester of analytic geometry or calculus is recommended, but not 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

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