Math 260 Linear Algebra
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The following dates are TENTATIVE and are subject to change, including for exams.
Textbook Chapter 1    
Syllabus (coming)   
Exam Solutions    
Octave   
Octave online   
Applications    
IMT    
Desmos matrix tool   
Office hours (coming)   
Do Good Sheet   
$5 Challenge
Day |
Date |
Before class |
In class supplements |
Homework to turn in at the beginning of our next class |
HW Sol. |
Section |
Video clips (length) |
HO |
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Mon |
1/13 |
Intro |
Don't be a slacker (do go the extra mile)  (7:38) |
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Coins Activity |
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Tue |
1/14 |
1.1 |
Row reduction  (7:36) |
1.1 |
Nutrition Planning Example
Examples of row reduction
Gauss-Jordan Elimination Website
Another Gauss-Jordan Elimination Website
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HW 1 |
Section 1.1, Page 10
2, 3, 8, 17, 18, 21 - 24,
26, 27, 30, 32 |
HW1 |
Thu |
1/16 |
1.2 |
Infinite solutions   (17:42)
No solution   (12:07) |
1.2 |
Some possible outcomes
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HW 2 |
Section 1.2, Page 21
1, 10 - 12, 15, 16, 19 - 23,
25 - 27, 30, 31 |
HW2 |
Fri |
1/17 |
1.3 |
Vectors   (5:48)
Linear combinations, span (20:34)
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1.3 |
Echelon forms: Span |
HW 3 |
Section 1.3, Page 32
6 - 13, 17, 20 - 27, 29,
31 (see 29), 32 |
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Mon |
1/20 |
No class |
MLK Day |
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Tue |
1/21 |
1.4 |
Matrix equation Ax = b (through 7:00)   (7:00) |
1.4 |
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HW 4 |
Section 1.4, Page 40
1, 4, 9, 13, 15, 16,
21 - 25, 28, 30 - 32 |
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Thu |
1/23 |
1.5 |
Parametric vector form   (24:45) |
1.5 |
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HW 5 |
Section 1.5, Page 47
2, 3, 6, 14, 18, 23 - 25,
29 - 33, 35 |
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Fri |
1/24 |
1.6 |
Balancing chemical equations   (4:41)
Network flow   (8:18)
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HW 6 |
Section 1.6, Page 54 3, 7 - 9, 11, 13 - 15 |
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Mon |
1/27 |
1.7 |
Linear independence   (15:45) |
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Echelon forms: Linear Ind. |
HW 7 |
Section 1.7, Page 60
2, 4, 11, 17 - 23,
25, 27, 34 - 39 |
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Tue |
1/28 |
1.8 |
Linear transformations   (13:52)
Image of a subset   (18:10)
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The matrix arcade
Visualizing linear transformations |
HW 8 |
Section 1.8, Page 68
2, 5, 8, 16, 18, 19, 21, 22,
29, 31, 33 - 35 |
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Thu |
1/30 |
1.9 |
The matrix of a linear transformation   (17:31)
Image of a linear transformation   (16:36)
Rotations, compositions, etc.   (10:03)
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Rotations, reflections, etc. |
HW 9 |
Section 1.9, Page 78
1 - 3, 7, 16, 23 - 26,
28, 36 |
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Fri |
1/31 |
Review |
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1.R |
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HW 10 |
Section 1.S, Page 88
1 (don't turn in), 5 - 8
10 - 12, 14 - 18, 21, 22
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Mon |
2/3 |
2.1 |
Matrix multiplication   (6:25)
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Excel matrix multiply, invert
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HW 11 |
Section 2.1, Page 100
2, 5, 7 - 9, 15, 16, 18, 20
21, 22, 24, 25 |
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Tue |
2/4 |
2.2 |
Matrix inverses   (14:14)
Use Matrix inverse to solve Ax = b   (6:39)
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Excel matrix multiply, invert
Row reduction tool
Derivation of 2 x 2 inverse formula
3 x 3 inverse formula
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HW 12 |
Section 2.2, Page 109
6, 7, 9, 10, 13, 16, 17,
19, 21, 22, 24, 35
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Thu |
2/6 |
2.3 |
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HW 13 |
Section 2.3, Page 115
5, 11 - 18, 20 - 24, 27, 41a
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Fri |
2/7 |
2.4, 2.5 |
Block/partioned matrices   (17:36)
LU factorization   (8:22)
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More on elementary matrices |
HW 14 |
Section 2.4, Page 121
6, 13, 25
Section 2.5, Page 129
2, 8, 9, 17
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Mon |
2/10 |
2.6 |
Input-output analysis   (6:04) |
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Excel consumption matrix example |
HW 15 |
Section 2.6, Page 136
1 - 4, 7, 8, 10, 12
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Tue |
2/11 |
Review |
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Section 2.S, Page 160
1 - 4, 6 - 10, 17, 19, 20
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Thu |
2/13 |
Exam 1 |
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Fri |
2/14 |
3.1 |
2 x 2 and 3 x 3 determinants   (10:00)
n x n determinants: part 1   (18:39)
n x n determinants: part 2   (9:02)
Shortcut for 3 x 3 determinants   (2:38)
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Determinant, etc. finder
3 x 3 inverse formula
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HW 16 |
Section 3.1, Page 167
2, 10, 16, 19, 20, 22, 26, 28
30, 34, 36, 38, 39, 40b, 41
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Mon |
2/17 |
3.2 |
Determinant after multiplying row   (13:19)
    (and correction)   (2:51)
Determinant after add row to another   (16:54)
Determinants after row operation   (10:24)
Determinant of triangular matrix   (8:06)
An example   (9:12)
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Why det(AB)=det(A)*det(B) |
HW 17 |
Section 3.2, Page 175
2, 4, 16 - 20, 22, 24, 27,
28, 31 - 33, 36 |
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Tue |
2/18 |
3.3 |
Determinant & area of a parallelogram   (21:37)
Cramer's Rule   (11:07)
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Visualizing Cramer's Rule |
HW 18 |
Section 3.3, Page 184
4, 6, 9, 10, 17, 18, 20, 22, 30
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Thu |
2/20 |
4.1 |
Vector spaces   (23:28) |
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HW 19 |
Section 4.1, Page 195
2, 5, 6, 8, 16, 21, 23
24, 27, 28, 32, 33 |
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Fri |
2/21 |
4.2 |
Null space   (10:22)
Calculating null space   (13:06)
Column space   (10:39)
Visualizing column space   (21:10)
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HW 20 |
Section 4.2, Page 205
2, 4, 7, 15, 23 - 26, 28
30, 39 |
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Mon |
2/24 |
4.3 |
Linear independence and null space   (9:31)
Bases for null space and column space   (25:12)
Pivot columns, basis for column space   (8:32)
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HW 21 |
Section 4.3, Page 213
2, 3, 5, 6, 8, 14, 19, 21 - 24
29 - 32
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Tue |
2/25 |
4.4 |
Coordinates   (16:07) |
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HW 22 |
Section 4.4, Page 222
4, 6, 13, 14, 18, 22,
23, 25, 27, 31
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Thu |
2/27 |
4.5 |
Nullity: dimension of nullspace   (13:58)
Rank: dimension of column space   (12:47)
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HW 23 |
Section 4.5, Page 229
4, 8, 11, 14, 19 - 21,
25, 26, 29 - 31
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Mon |
2/28 |
4.6 |
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HW 24 |
Section 4.6, Page 236
1, 2, 5 - 27
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Mon |
3/10 |
4.9 |
Origin of Markov Chains   (7:14)
Markov Chain matrix   (12:49)
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Chutes and Ladders and Markov Chains |
HW 25 |
Section 4.9, Page 260
2, 3, 6, 7, 12, 13, 17,
18, 20, 21
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Tue |
3/11 |
5.1 |
Eigenvectors & e.values (through 2:16)   (2:16)
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Visualizing eigenvectors in Excel
Eigenvalue/vector finder
Proof by induction video
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HW 26 |
Section 5.1, Page 271
3, 4, 6, 10, 13, 16, 17,
21 - 25, 27, 30, 33
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Thu |
3/13 |
5.2 |
How to find eigenvalues  (4:35)  
x→
is ≠ 0
Find eigenvalues example   (5:38)
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HW 27 |
Section 5.2, Page 279
2, 8, 11, 16, 19, 21,
22, 25, 26
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Fri |
3/14 |
5.3 |
Matrix diagonalization   (11:36) |
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HW 5.3.33 in Wolfram Alpha |
HW 28 |
Section 5.3, Page 286
2, 4, 5, 10, 11, 21 - 24
26 - 31
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Mon |
3/17 |
Review |
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Review Example
Proof that stochastic matrix e-values ≤ 1 |
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Section 5.S, Page 262
Don't turn in--for practice
1, 2, 3, 8, 9, 11 - 13
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Tue |
3/18 |
Exam 2 |
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Thu |
3/20 |
5.6 |
Predator-prey systems   (5:08)
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Predator-prey spreadsheet |
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Fri |
3/21 |
5.6 |
Predator-prey example   (10:16) |
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Predator-prey spreadsheet |
HW 29 |
Section 5.6, Page 309
1, 2, 6 - 8, 10, 13
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Mon |
3/24 |
5.7 |
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A Covid modelling example |
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Tue |
3/25 |
5.7 |
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HW 30 |
Section 5.7, Page 317
1, 2, 5 - 8
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Thu |
3/27 |
5.8 |
Power Method   (8:59) |
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Power Method spreadsheet |
HW 31 |
Section 5.8, Page 321
1, 4 - 6, 8, 9, 19 - 21
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Fri |
3/28 |
6.1 |
Orthogonal complements   (6:00) |
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Proof of The Law of Cosines |
HW 32 |
Section 6.1, Page 336
2, 3, 6, 7, 10, 14, 17,
19, 20, 22, 23, 25, 28, 30
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Mon |
3/31 |
6.2 |
Orthogonal sets  (11:55) |
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HW 33 |
Section 6.2, Page 344
2, 3, 8, 9, 12, 14, 21,
23 - 25, 27 - 30
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Tue |
4/1 |
6.3 |
Orthogonal bases   (5:59) |
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HW 34 |
Section 6.3, Page 352
1, 6, 8, 12, 13, 16, 19 - 24
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Thu |
4/3 |
6.4 |
Gram-Schmidt Process   (19:23) |
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HW 35 |
Section 6.4, Page 358
3, 4, 7 - 10, 17 - 19
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Fri |
4/4 |
6.5 |
Least squares example (13:24) |
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Calculus-based least sq. derivation
History of least squares
Ellipses and least squares
Summary of Least Squares
Summary of Techniques for Ax = b
    and   Eight Examples
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HW 36 |
Section 6.5, Page 366
3, 4, 7, 8, 10, 11, 13,
17, 18, 22, 25
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Mon |
4/7 |
6.6 |
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HW 37 |
Section 6.6, Page 374
2, 7 - 10, 13, 16
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Tue |
4/8 |
6.7 |
Inner Product Spaces   (12:08) |
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Almost inner products |
HW 38 |
Section 6.7, Page 382
2, 3, 5, 7, 13, 15 - 26
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Thu |
4/10 |
Review |
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Fri |
4/11 |
6.8 |
Intro to Fourier Series   (13:52) |
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What are Fourier Series?   (8:24)
What are Fourier Series? Another view  (19:42) |
HW 39 |
Section 6.8, Page 389
5 - 12 |
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Mon |
4/14 |
Exam 3 |
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Tue |
4/15 |
7.1 |
Column by row matrix multiplication   (12:02)
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HW 40 |
Section 7.1, Page 399
2, 4, 6, 8 - 10, 14, 17,
24 - 29, 31, 34, 35
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Thu |
4/17 |
IM |
Jacobi Method (6:50)
Gauss-Seidel Method (6:04) |
IM1 |
Jacobi, Gauss-Seidel, SOR Spreadsheet |
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Fri |
4/18 |
IM |
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IM2 | |
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Mon |
4/21 |
IM |
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Tue |
4/22 |
Review |
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Mon |
4/21 |
Review |
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Tue |
4/22 |
Review |
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Thu |
5/1 |
Final Exam 1:30 - 4:00 p.m. |
HW Problems to work in class
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