MA 751 Supplementary materials
Course Announcement -
Course information and notes –
Syllabus
Projects
Notes on matrix notation
Midterm Solutions (Spring 2021)
Note on Bayesian statistics (Section 8.3)
Midterm Solutions (Spring 2022)
Final Exam Solutions (Spring 2021)
Problem set |
Due date |
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Feb. 3 |
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Feb. 10 |
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Feb. 17 |
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Feb. 24 |
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March
1 |
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March
4 |
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March
17 |
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█ Midterm test |
March
24 |
March
31 |
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April
5 |
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April
8 |
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April
14 |
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April 21 |
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April
28 |
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(Note PS 11 does need not to be turned in) |
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May
4 |
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(Note PS 12 parts 1 and 2 need not be turned in) |
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May
4 |
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Lecture |
Lecture Notes |
Day |
Introduction |
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Introduction |
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Lecture
1 |
Thursday |
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Lecture
2 |
Tuesday |
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Lecture
3 |
Thursday |
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Lecture
4 |
Tuesday |
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Lecture
5 |
Thursday |
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Lecture
6 |
█ Feb. 8 |
Tuesday |
Lecture
7 |
█ Feb. 10 |
Thursday |
Lecture
8 |
Tuesday |
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Lecture
9 |
Thursday |
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Lecture
10 |
Thursday |
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Lecture
11 |
█ Mar. 1 |
Tuesday |
Lecture
12 |
█ Mar. 3 |
Thursday |
Lecture
13 |
█ Mar. 15 |
Tuesday |
Lecture
14 |
█ Mar. 17 |
Thursday |
Lecture
15 |
█ Mar. 22 |
Tuesday |
Test |
█ Mar. 24 |
Thursday |
Lecture
16 |
█ Mar. 29 |
Tuesday |
Lecture
17 |
█ Mar. 31 |
Thursday |
Lecture
18 |
█ Apr. 5 |
Tuesday |
Lecture
19 |
█ Apr. 7 |
Thursday |
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Lecture
20 |
█ Apr. 12 |
Tuesday |
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(Note the first movie
shows how Gaussian kernel width affects SVM classification - kernel width
narrows with time. Second movie shows
how changing the margin parameter C affects the classification – margin
narrows with time) |
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Lecture
21 |
█ Apr. 14 |
Thursday |
Lecture
22 |
█ Apr. 19 |
Tuesday |
Lecture
23 |
█ Apr. 21 |
Thursday |
Lecture
24 |
█ Apr. 26 |
Tuesday |
Lecture
25 |
█ Apr. 28 |
Thursday |
Lecture
26 |
█ May 3 |
Tuesday |
Lecture
11C |
Optional lecture: not covered in problem sets or exams |
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Lecture
11D |
Optional lecture: not covered in problem sets or exams |
Optional additional material and examples
2. Decision trees and random forests
Lecture Notes -
1. The three
pillars of machine learning
2. Probability and Measure Theory
5.
Statistical machine learning and infinite dimensions
6. Measure
spaces and Hilbert spaces
7. Reproducing
kernel Hilbert Spaces
8. Support
vector machines (SVM) (optional)
Optional additional material and examples
Resources –
Neural networks: Funahashi’s Theorem