Math 220: Matrices, Sections 1, 8, and 11

Instructor: Associate Professor Tim Reluga

Office: 424 McAllister

Office Hours: Thursday afternoons, 1:30 - 3:30 pm

Textbook: Linear Algebra and Its Applications, 5th edition, by Lay, Lay, and McDonald, accessed online through Course ID's are as follows.

The course webpage for all sections, including full syllabus and learning objectives.


(details TBA)

Course description

Many problems we have to solve in day-to-day business, engineering, and science practice require the simultaneous study of several different but interrelated factors. Although problems of this form have been studied throughout the long history of mathematics, only in the early 20th century did the systematic approach we now refer to as linear algebra based on matrices emerge. Matrices and linear algebra are now recognized as the fundamental tool for foundational methods in statistics, optimization, quantum mechanics, and many other fields, and are an essential component of most subfields of mathematics. Linear algebra provides students their first introduction to the concept of dimension in an abstract setting where things with 4, 5, or even more dimensions are often encountered. MATH 220 is a 2 credit course that teaches the core concepts of matrix arithmetic and linear algebra. It is a required course for many students majoring in engineering, science, or secondary education. In past coursework, students should have gained practice solving pairs of equations like 3 x + 4 y = 10, x - y = 1. This is a system of two linear equations with two unknowns and as a unique solution students can find by isolating and substituting. In linear algebra, this system is represented as A x = b, where x is a vector of unknowns, A is a matrix, and b is a vector of constants. Linear algebra is the field of mathematics that grew out of a need to solve systems like these and related problems with many unknown variables. Topics covered in MATH 220 include matrix algebra, vectors, linear transformations, solution to systems of linear equations, determinants, matrix inverses, concepts of rank and dimension, eigenvalues, eigenvectors, and others as time permits.

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