Machine Learning (CSCI 1420/ENGN 2520)

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Lecture calendar

Lecture Date Topic Lecture notes Reference (book sections)

1

January 26

Introduction

notes

2

January 31

Linear regression, basis functions, least squares

notes

1.1, 3.1

3

February 2

Special DSI lecture, Chris Danforth (UVM)

4

February 7

Maximum likelihood view of linear regression, outliers

notes

3.1

5

February 14

Robust regression and Linear Programming

pff's notes, regular notes

6

February 16

Classification, Bayesian Decision Theory

notes

1.5

7

February 23

Estimating distributions (parametric and non-parametric)

notes

2.1, 2.2, 2.3, 2.5

8

February 28

Parzen windows, Bayesian estimation, predictive distribution

notes

2.1, 2.2, 2.3, 2.5

9

March 2

Linear separators, Perceptron Algorithm

notes

4.1, 4.1.7

10

March 7

Max-margin separator, linear support vector machines

notes

7.1

11

March 9

Gradient descent for linear SVM, Multiclass problems

notes

7.1

12

March 16

Kernel methods

notes 6

13

March 21

PAC learning

notes

14

March 23

PAC learning

notes

15

April 4

clustering, K-means

notes 9

16

April 11

Mixture of Gaussians, EM

notes 9

17

April 13

Principal Component Analysis

notes 12

18

April 18

Random projections

notes

19

April 20

Guest lecture, Katherine Kinnaird

20

April 25

Neural Networks

notes 5

21

April 27

Neural Networks

notes 5