Math 260

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Day Date Before class In class
supplements
Homework to turn in
before next class
HW
Sol.
Section Video clips (length) HO   HW  
Mon 8/17 Intro Don't be a slacker (do go the extra mile)  (7:38) Coins Activity
Tue 8/18 1.1 Row reduction  (7:36) 1.1 1.1
2, 8
Row reduction tool
Another row reduction tool
HW
1
Section 1.1, Page 10
2, 3, 8, 17, 18, 21 - 24,
26, 27, 30, 32
HW1
Thu 8/20 1.2 Infinite solutions   (17:42)
No solution   (12:07)
1.2 1.2
10, 16
Row reduction tool
Some possible echelon forms
HW
2
Section 1.2, Page 21
1, 10 - 12, 15, 16, 19 - 23,
25 - 27, 30, 31, 32
HW2
Fri 8/21 1.3 Vectors   (5:48)
Linear combinations, span (20:34)
1.3 1.3
6, 12
Echelon forms: Span HW
3
Section 1.3, Page 32
6 - 13, 17, 20 - 27, 29,
31 (see 29), 32
HW3
Mon 8/24 1.4 Matrix equation Ax = b (through 7:00)   (7:00) 1.4 1.4
4, 22
HW
4
Section 1.4, Page 40
1, 4, 9, 13, 15, 16,
21 - 25, 28, 30 - 32
HW4
Tue 8/25 1.5 Parametric vector form   (24:45) 1.5 None HW
5
Section 1.5, Page 47
2, 3, 6, 14, 18, 23 - 25,
29 - 33, 35
HW5
Thu 8/27 1.6 Balancing chemical equations   (4:41)
Network flow   (8:18)
1.6 None HW
6
Section 1.6, Page 54
3, 7 - 9, 11, 13 - 15
HW6
Fri 8/28 1.7 Linear independence   (15:45) 1.7 1.7
16 - 19
Echelon forms: Linear Ind. HW
7
Section 1.7, Page 60
2, 4, 11, 17 - 23,
25, 27, 34 - 39
HW7
Mon 8/31 1.8 Linear transformations   (13:52)
Image of a subset   (18:10)
1.8 1.8
2, 8
Visualizing linear transformations HW
8
Section 1.8, Page 68
2, 5, 8, 16, 18, 19, 21, 22,
29ab, 31, 33 - 35
HW8
Tue 9/1 1.9 The matrix of a linear transformation   (17:31)
Image of a linear transformation   (16:36)
Rotations, compositions, etc.   (10:03)
1.9 1.9
6, 18
Rotations, reflections, etc. HW
9
Section 1.9, Page 78
1 - 3, 7, 16, 23 - 26,
28, 36
HW9
Thu 9/3 Review 1.R None HW
10
Section 1.S, Page 88
1 (don't turn in), 5 - 8
10 - 12, 14 - 18, 21, 22
HW10
Fri 9/4 2.1 Matrix multiplication   (6:25) 2.1 2.1
5, 8
Excel matrix multiply, invert HW
11
Section 2.1, Page 100
2, 5, 7 - 9, 15, 16, 18, 20
21, 22, 24, 25
HW11
Mon 9/7 No class
Tue 9/8 2.2 Matrix inverses   (14:14)
Use Matrix inverse to solve Ax = b   (6:39)
2.2 2.2
10
Excel matrix multiply, invert
Row reduction tool
Derivation of 2 x 2 inverse formula
HW
12
Section 2.2, Page 109
6, 7, 9, 10, 13, 16, 17,
19, 21, 22, 24, 35
HW12
Thu 9/10 2.3 2.3 2.3
12
HW
13
Section 2.3, Page 115
5, 11 - 18, 20 - 24, 27, 41
HW13
Fri 9/11 2.4, 2.5 Block/partioned matrices   (15:19)
LU factorization   (8:22)
2.4
2.5
None Elem. matrices for LU factorization HW
14
Section 2.4, Page 121
6, 13, 25
Section 2.5, Page 129
2, 8, 9, 17
HW14
Mon 9/14 2.6 Input-output analysis   (6:04) 2.6 None Excel consumption matrix example HW
15
Section 2.6, Page 136
1 - 4, 7, 8, 10, 12
HW15
Tue 9/15 Review Section 2.S, Page 160
Don't turn in--for practice
1 - 4, 6, 8 - 11, 17 - 20
Thu 9/17 Exam 1
Fri 9/18 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)
3.1 3.1
26, 30
Determinant, etc. finder
3 x 3 inverse formula
HW
16
Section 3.1, Page 167
2, 10, 16, 19, 20, 22, 26, 28
30, 34, 36, 38, 39, 40b, 41
HW16
Mon 9/21 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)
3.2 3.2
6
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
HW17
Tue 9/22 3.3 Determinant & area of a parallelogram   (21:37)
Cramer's Rule   (11:07)
3.3 3.3
4
Visualizing Cramer's Rule HW
18
Section 3.3, Page 184
4, 6, 9, 10, 17, 18, 20, 22, 30
HW18
Thu 9/24 4.1 Vector spaces   (23:28) 4.1 None HW
19
Section 4.1, Page 195
2, 5, 6, 8, 16, 21, 23
24, 27, 28, 32, 33
HW19
Fri 9/25 4.2 Null space   (10:22)
Calculating null space   (13:06)
Column space   (10:39)
Visualizing column space   (21:10)
4.2 4.2
24
HW
20
Section 4.2, Page 205
2, 4, 7, 15, 23 - 26, 28
30, 39
HW20
Mon 9/28 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)
4.3 4.3
8
HW
21
Section 4.3, Page 213
2, 3, 5, 6, 8, 14, 19, 21 - 24
29 - 32
HW21
Tue 9/29 4.4 Coordinates   (16:07) 4.4 4.4
4, 6
HW
22
Section 4.4, Page 222
4, 6, 13, 14, 18, 22,
23, 25, 27, 31
HW22
Thu 10/1 4.5 Nullity: dimension of nullspace   (13:58)
Rank: dimension of column space   (12:47)
4.5 4.5
8
HW
23
Section 4.5, Page 229
4, 8, 11, 14, 19 - 21,
25, 26, 29 - 31
HW23
Fri 10/2 4.6 4.6 4.6
2
HW
24
Section 4.6, Page 236
1, 2, 5 - 27
HW24
Mon 10/5 4.9 Origin of Markov Chains   (7:14)
Markov Chain matrix   (12:49)
4.9 4.9
6
HW
25
Section 4.9, Page 260
2, 3, 6, 7, 12, 13, 17,
18, 20, 21
HW25
Tue 10/6 5.1 Eigenvectors & e.values (through 2:16)   (2:16)
5.1 5.1
4
Visualizing eigenvectors in Excel
Eigenvalue/vector finder
Proof by induction video
HW
26
Section 5.1, Page 271
3, 4, 6, 10, 13, 16, 17,
21 - 25, 27, 30, 33
HW26
Thu 10/8 5.2 How to find eigenvalues  (4:35)   x is 0
Find eigenvalues example   (5:38)
5.2 5.2
2
HW
27
Section 5.2, Page 279
2, 8, 11, 16, 19, 21,
22, 25, 26
HW27
Fri 10/9 5.3 Matrix diagonalization   (11:36) 5.3 5.3
2
HW 5.3.33 in Wolfram Alpha HW
28
Section 5.3, Page 286
2, 4, 5, 10, 11, 21 - 24
26 - 31
HW28
Mon 10/12 Review HW
29
Section 4.S, Page 262
1 (don't turn in), 2, 3,
8, 9, 11 - 13
HW29
Tue 10/13 6.1 Orthogonal complements   (6:00) 6.1 HW 30 not due until Monday HW
30
Section 6.1, Page 336
2, 3, 6, 7, 10, 14, 17,
19, 20, 22, 23, 25, 28, 30
HW30
Thu 10/15 Exam 2
Fri 10/16 5.6 Predator-prey systems   (5:08)
5.6 Predator-prey spreadsheet
Mon 10/19 5.6 Predator-prey example   (10:16) Predator-prey spreadsheet HW
31
Section 5.6, Page 309
1, 2, 6 - 8, 10, 13
HW31
Tue 10/20 5.7 5.7
Thu 10/22 5.7 HW
32
Section 5.7, Page 317
1, 2, 5 - 8
HW32
Fri 10/23 No class
Mon 10/26 5.8 Power Method   (8:59) 5.8 Power Method spreadsheet HW
33
Section 5.8, Page 321
1, 4 - 6, 8, 9, 19 - 21
HW33
Tue 10/27 6.2 Orhogonal sets  (11:55) 6.2 HW
34
Section 6.2, Page 344
2, 3, 8, 9, 12, 14, 21,
23 - 25, 27 - 30
HW34
Thu 10/29 6.3 Orthogonal bases   (5:59) 6.3 HW
35
Section 6.3, Page 352
1, 6, 8, 12, 13, 16, 19 - 24
HW35
Fri 10/30 6.4 Gram-Schmidt Process   (19:23) 6.4 HW
36
Section 6.4, Page 358
3, 4, 7 - 10, 17 - 19
HW36
Mon 11/2 6.5 Least squares example (13:24) 6.5
6.6
Calculus-based least sq. derivation HW
37
Section 6.5, Page 366
3, 4, 7, 8, 10, 11, 13,
17, 18, 22, 25
HW37
Tue 11/3 6.6 Summary of Least Squares
Summary of Techniques for Ax = b
    and   Eight Examples
HW
38
Section 6.6, Page 374
2, 7 - 10, 13, 16
HW38
Thu 11/5 Review
Fri 11/6 Exam 3
Mon 11/9 6.7 Inner Product Spaces   (12:08) 6.7 HW
39
Section 6.7, Page 382
2, 3, 5, 7, 13, 15 - 26
HW39
Tue 11/10 6.8 Intro to Fourier Series   (13:52) 6.8 What are Fourier Series?   (8:24)
What are Fourier Series? Another view  (19:42)
HW
40
Section 6.8, Page 389
5 - 12
HW40
Thu 11/12 7.1 Column by row matrix multiplication   (12:02) 7.1 HW
41
Section 7.1, Page 399
2, 4, 6, 8 - 10, 14, 17,
24 - 29, 31, 34, 35
HW41
Fri 11/13 IM1 Jacobi Method (6:50)
Gauss-Seidel Method (6:04)
IM1 Jacobi, Gauss-Seidel, SOR Spreadsheet
Mon 11/16 IM2 IM2
Tue 11/17 TBA
Thu 11/19 No class Work on take-home exam
The final exam will be take-home and will be due by 4:00 pm (California time) Friday, November 22.