Advent 2007 - CS430 Machine Learning - Lankewicz

Syllabus

Date

Class

Chapter

Topic

Assignment
Due

 

 

 

 

 

Th Aug 30

1

1

Introduction to Machine Learning

 

T Sep 4

2

2

Concept Learning

 

Th Sep 6

3

3

Decision Tree Learning

 

T Sep 11

4

3

Decision Tree Learning

 

Th Sep 13

5

4

Neural Networks

 

T Sep 18

6

4

Neural Networks

 

Th Sep 20

7

 

 

 

T Sep 25

8

 

ID3 Demo

ID3

Th Sep 27

9

4

Neural Networks

 

T Oct 2

10

5

Research Methodology: Data Representation, Sampling

 

Th Oct 4

11

5

Research Methodology: Evaluating Hypotheses

 

T Oct 9

12

Examination 1

 

Th Oct 11

13

5

Research Methodology: Comparing Algorithms

 

T Oct 16

14

 

Neural Network Demo

Neural Network

 

 

 

Mid-Semester

 

Th Oct 18

15

6

Bayesian Learning

 

 

 

 

Fall Break

 

Th Oct 25

16

6

Bayesian Belief Networks

 

T Oct 30

17

 

Bayesian Demo

Bayesian

Th Nov 1

18

Examination 2

 

T Nov 6

19

Non-supervised Learning: Clustering

Th Nov 8

20

Non-supervised Learning: Clustering

T Nov 13

21

8

Nonparametric Methods: Instance-based Learning

 

Th Nov 15

22

8

Nonparametric Methods: Instance-based Learning

 

T Nov 20

23

9

Genetic Algorithms

 

 

 

 

Thanksgiving

 

T Nov 27

24

9

Genetic Algorithms

 

Th Nov 29

25

 

Project Design

 

T Dec 4

26

 

Project Design

 

Th Dec 6

27

 

Project Presentation

Project

T Dec 11

28

 

Overview, Course Evaluation