Introduction to Linear Algebra (Math 301)
Linear algebra from a matrix perspective with applications from the applied sciences. Topics include the algebra of matrices, methods for solving linear systems of equations, eigenvalues and eigenvectors, matrix decompositions, vector spaces, linear transformations, least squares, and numerical techniques. Prerequisites: Math 170, Math 175.
 Basic course information
 Required textbook and other resources
 Lectures
 Homework assignments
 Exams
 Grading policy
Send me an email
Please send me an email at donnacalhoun@boisestate.edu so that I can compile an email list for the class. At the very least, include a subject header that says "Math 301". You may leave the message area blank, if you wish, or send me a short note about what you hope to get out of this course.
Basic course information
Instructor  Prof. Donna Calhoun 
Office  Mathematics 241A 
Time  Tuesday/Thursday 10:3011:45 
Place  Engineering Building Rm 313 
Office Hours  Wednesday 1:30PM3:30PM, or by appointment 
Prerequesites  Math 175 
Required textbook and other resources
 Linear Algebra with Applications, Second Edition, by Jeffrey Holt. W. H. Freeman, (2017) (required).
 WebAssign, by . (required).
Lectures
We will stick the following schedule as much as possible.
Week #1 (Jan. 8) 
Tuesday 
Introduction to Linear Algebra; Sections 1.1
Thursday 
Section 1.2 : Linear Systems and Matrices

Week #2 (Jan. 15) 
Tuesday 
Section 1.3 : Applications of linear systems
Thursday 
Section 2.1 : Vectors

Week #3 (Jan. 22) 
Tuesday 
Section 2.2 : Span
Thursday 
More on Span; Section 2.3 : Linear Independence

Week #4 (Jan. 29) 
Tuesday 
Section 2.3 : Linear Independence
Thursday 
Section 3.1 : Linear Transformation

Week #5 (Feb. 5) 
Tuesday 
Linear transformations (continued)
Thursday 
Section 3.2 : Linear Algebra

Week #6 (Feb. 12) 
Tuesday 
Review for Midterm #1
Thursday 
Midterm #1

Week #7 (Feb. 19) 
Tuesday 
3.4 : LU Factorization
Thursday 
3.4 : LU (Continued)

Week #8 (Feb. 26) 
Tuesday 
Section 4.1 : Introduction to Subspaces;
Section 4.2 : Basis and Dimension
Thursday 
Section 4.2 : Basis and Dimension

Week #9 (Mar. 6) 
Tuesday 
Midterm #2
Thursday 
No Class!

Week #10 (Mar. 12) 
Tuesday 
Section 5.1 : The Determinant Function
Thursday 
Section 5.2 : Properties of the Determinant

Week #11 (Mar. 19) 
Tuesday 
Section 5.3 : Applications of the Determinant
Thursday 
TBA

Week #12 (Apr. 2) 
Tuesday 
Section 6.1 : Eigenvalues and Eigenvectors
Thursday 
Section 6.2 : Diagonalization

Week #13 (Apr. 9) 
Tuesday 
Section 6.3 : Complex eigenvalues and eigenvectors
Thursday 
Section 6.4 : Differential Equations

Week #14 (Apr. 16) 
Tuesday 
TBA
Thursday 
Midterm #3

Week #15 (Apr. 23) 
Tuesday 
Review for Final
Thursday 
Review for Final

Homework assignments
Homework assignments are to be done on WebAssign.
Homework #1 
Due Jan. 19, 5PM This assignment is on WebAssign 
Homework #2 
Due Jan. 26, 5PM This assignment is on WebAssign 
Homework #3 
Due Feb. 6, 5PM This assignment is on WebAssign 
Homework #4 
Due Feb. 11, 5PM This assignment is on WebAssign 
Homework #5 
Due Mar. 5, 5PM This assignment is on WebAssign 
Homework #6 
Due Mar. 6, 5PM This assignment is on WebAssign 
Homework #7 
Due Mar. 20 5PM This assignment is on WebAssign 
Homework #8 
Due Mar. 25 5PM This assignment is on WebAssign 
Homework #9 
Due Apr. 10 5PM This assignment is on WebAssign 
Homework #10 
Due Apr. 18 5PM This assignment is on WebAssign 
Exams
We will have two midterms and one final exam
Midterm #1  Date: Thursday, Feb. 15 
Midterm #2  Date: Tuesday, March 6 
Midterm #3  Date: Thursday, April 19 
Final  Date: Thursday May 3rd 10AM  12PM 
You can find the Final Exam calendar here.
Grading policy
Homework will count for 20% of your final grade, and the three midterms and final will count for 80%. A 90% and above will earn you an A, between 80% and 90% will earn you at least a B, between 70% and 80% will be at least a C, and below 60% will be a D or F. If there is any deviation from this grading policy, it will be to lower the percentages, i.e. you could still earn an A with less than 90%, but you will never need more than 90%.