Machine Learning (ENGN 2520)

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Lecture calendar

Lecture Date Topic Reference (book sections)

1

September 5

Introduction

2

September 10

Least squares regression

1.1, 3.1

3

September 12

Least squares regression

3.1

4

September 17

Robust regression and linear programming

notes

5

September 19

Regulatization, Classifiers

6

September 24

Decision Theory, Bayes optimal classifier

1.5

7

September 26

Estimating distributions (parametric and non-parametric)

2.1, 2.2, 2.3, 2.5

8

October 1

Parzen windows, Bayesian estimation, predictive distribution

2.1, 2.2, 2.3, 2.5

10

October 8

Linear separators, perceptron algorithm

4.1, 4.1.7

11

October 10

Support vector machines, gradient descent

7.1

12

October 15

Stochastic gradient descent, non-binary classification

13

October 17

Multiclass SVM, Kernels

14

October 22

PAC learning

15

October 24

PAC learning

16

October 29

Clustering, K-means

9

17

October 31

Mixture of Gaussians

9

18

November 5

Principal Component Analysis

12

19

November 7

Random Projections

20

November 12

Neural Networks

5

21

November 14

Neural Networks

5

22

November 19

Neural Network Architectures

23

November 21

Adversarial Examples, Bias in ML

ICERM public lecture

24

November 26

Boosting

25

December 3

Nearest Neighbors

26

December 5

Recommendation systems, Collaborative Filtering, Matrix completion