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MA 751 Supplementary materials

 

Course Announcement -    

Announcement

Course information and notes – 

Syllabus
Projects
Notes on matrix notation

 

 

             Problem set

Due date

 

 

  Problem Set 1

    Feb. 4

   Suggestions, PS 1

 

  Problem Set 2

   Feb. 11

   Suggestions, PS 2

 

  Problem Set 3

    Feb. 18

   Suggestions, PS 3

 

  Problem Set 4

     Feb. 25

   Suggestions1.0, PS 4

   Suggestions2.0, PS 4

   Suggestions3.0, PS 4

Suggestions1.0 are the original ones;

Suggestions3.0 are the final iteration

  Problem Set 5

    March 3

   Suggestions, PS 5

 

  Data Assignment 1

    March 8

 

Lecture

Lecture Notes

Day

Introduction

Overview

 

Introduction

Probability

 

 

 

 

Lecture 1

 Jan. 26

    Tuesday

Lecture 2

   Jan. 28

    Thursday

Lecture 3

   Feb. 2

    Tuesday

Lecture 4

   Feb. 4

    Thursday

Lecture 5

   Feb. 9

    Tuesday

Lecture 6

   Feb. 11

    Thursday

Lecture 7

   Feb. 18

    Thursday

Lecture 8

   Feb. 23

    Tuesday

Lecture 9

   Feb. 25

    Thursday

 

 

 

 

 

 

 

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 –

Wavelets: older review

Park and Sandberg paper

Park and Sandberg notes

Cucker and Smale

Poggio article