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MA 751 Supplementary materials

 

Course Announcement -    

Announcement

Course information and notes – 

Syllabus
Projects
Notes on matrix notation

Midterm Test (Spring 2021)

Midterm Solutions (Spring 2021)

Note on Bayesian statistics (Section 8.3)

Midterm Test (Spring 2022)

Midterm Solutions (Spring 2022)

Final Exam (Spring 2021)

Final Exam Solutions (Spring 2021)

 

 

 

 

 

             Problem set

Due date

 

 

  Problem Set 1

    Feb. 3

   Suggestions, PS 1

 

  Problem Set 2

   Feb. 10

   Suggestions, PS 2

 

  Problem Set 3

    Feb. 17

   Suggestions, PS 3

 

  Problem Set 4

     Feb. 24

   Suggestions, PS 4

  Data Assignment 1

    March 1

  Problem Set 5

    March 4

   Suggestions, PS 5

 

  Problem Set 6

    March 17

   Suggestions, PS 6

 

  Midterm test

    March 24

  Problem Set 7

    March 31

   Suggestions, PS 7

 

  Data Assignment 2

    April 5

  Problem Set 8

    April 8

   Suggestions, PS 8

 

  

 

  Problem Set 9

   April 14

   Suggestions, PS 9

 

  Problem Set 10

   April 21

   Suggestions, PS 10

 

  Problem Set 11

    April 28

   Suggestions, PS 11

(Note PS 11 does need not to be turned in)

  Problem Set 12

   May 4

   Suggestions, PS 12

(Note PS 12 parts 1 and 2 need not be turned in)

  Problem Set 12, pt. 2

   May 4

   Suggestions, PS 12 pt. 2

 

 

Lecture

Lecture Notes

Day

Introduction

Overview

 

Introduction

Probability

 

 

 

 

Lecture 1

 Jan. 20

    Thursday

Lecture 2

   Jan. 25

    Tuesday

Lecture 3

   Jan. 27

    Thursday

Lecture 4

   Feb. 1

    Tuesday

Lecture 5

   Feb. 3

    Thursday

Lecture 6

   Feb. 8

    Tuesday

Lecture 7

   Feb. 10

    Thursday

Lecture 8

   Feb. 15

    Tuesday

Lecture 9

   Feb. 17

    Thursday

Lecture 10

   Feb. 24

    Thursday

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

 

Note on Bayesian statistics (Section 8.3)

 

Lecture 20

   Apr. 12

    Tuesday

 

Vert movie 1; Vert movie 2;  Aharoni nonlinear SVM video

(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)

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

SVM

Optional lecture: not covered in  problem sets or exams

Lecture 11D

Solving SVM

Optional lecture: not covered in  problem sets or exams

 

Optional additional material and examples

1.  More on Boosting

2. Decision trees and random forests

3. Details of random forests

 

Lecture Notes -  

1.  The three pillars of machine learning

2. Probability and Measure Theory

3.  Linear Algebra Primer

4.  Inner Products

5.  Statistical machine learning and infinite dimensions

6.  Measure spaces and Hilbert spaces

7.  Reproducing kernel Hilbert Spaces

8.  Support vector machines (SVM) (optional)

9.  Solving SVM (optional)

 

Optional additional material and examples

 

Resources –

Neural networks: Funahashi’s Theorem

Wavelets: older review

Park and Sandberg paper

Park and Sandberg notes

Cucker and Smale

Poggio article