Machine Learning (CSCI 1420/ENGN 2520)

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Lecture calendar

Lecture Date Topic Reference (book sections)

1

January 22

Introduction

2

January 29

Linear regression, basis functions, least squares

1.1, 3.1

3

February 3

Maximum likelihood view of linear regression, outliers

3.1

4

February 5

Robust regression via Linear Programming

brief notes

5

February 10

Classification, Bayesian Decision Theory, MLE for Bernoulli

1.5.2, 2.1

6

February 12

MLE for Bernoulli, Multinomial, Multivariate Gaussian

2.1, 2.2, 2.3

7

February 19

Bayesian estimation and predictive distribution

2.1, 2.2, 2.3

8

February 24

Linear separators, perceptron algorithm

4.1, 4.1.7

9

March 3

Max margin separators, linear support vector machines

7.1

10

March 5

Gradient descent for linear SVM, Multiclass problems

7.1

11

March 10

Kernel Methods

6, 6.1, 6.2, 7.1

12

March 12

PAC learning, finite hypothesis spaces, Boolean functions

[1]

13

March 17

PAC learning threshold functions, infinite hypothesis spaces, VC dimension

[1]

14

March 31

Bayesian Networks

8.1

15

April 2

Bayesian Networks

8.1

16

April 7

Hidden Markov Models

Rabiner Survey

17

April 9

Hidden Markov Models

Rabiner Survey

18

April 14

Clustering, K-means

9.1, 9.2

19

April 16

Mixture of Gaussians, EM

9.4

20

April 21

PCA, LDA

12.1, 4.1.4

21

April 23

Parzen windows, Nearest-neighbor methods

22

April 28

Neural Networks

[1] An Introduction to Computational Learning Theory. Kerns and Vazirani.