Welcome Hacker Dojo Machine Learning Class Fall 2010
Organizer: Doug Chang
Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman
We've put together a calendar for the next three machine learning courses that we're going to teach. Our objective for the sequence is to enable you to read the current literature, to implement algorithms based on what you read and to extend or modify methods in the current machine learning oeuvre in order to suit the needs of your particular problems. The first course in this sequence will cover basic techniques in ML at sufficient depth that you will be able to immediately apply the techniques you've learned to real problems.
We're going to use R as our lingua franca for looking at homework problems, discussing them and comparing different solution approaches. Load R onto your laptop or desk computer before you come to the first class. http://cran.rproject.org/ We will include some descriptive material on using R in the first two lectures in order to get everyone up to speed on it. References for R are here: References for R Comment on these references here: Reference for R Comments
Easy access to The Google Group
General Calendar for the Year:
Fall 2010: Basic Machine Learning
Book: Introduction to Data Mining by PangNing Tan et al
Winter 2010: Advanced Machine Learning
Books: Professor Andrew Ng's lecture notes from CS229 and Elements of Statistical Learning, Hastie et al
Spring 2011: Extended Machine Learning Project (Competition)
Complete Outline for the first Course (Fall 2010):
1st Week 


22Sep 
Chapter 1 & 2 NotesForFirstClass

Papers 
23Sep 
Chapter 3 

2nd Week 


29Sep 
Chapter 4 
HW # 1 HW1.pdf Due 
30Sep 
Chapter 4 

3rd Week 


6Oct 
Simple Regression 
HW #2 Due 
7Oct 
Ridge Regression 

4rd Week 


13Oct 
Chapter 5 
HW #3 Due 
14Oct 
Chapter 5 

5th Week 


20Oct 
Chapter 5 
HW #4 Due 
21Oct 
Chapter 5 

6th Week 


27Oct 
Chapter 5 
HW #5 Due 
28Oct 
Chapter 6 

7th Week 


3Nov 
Chapter 6 
HW #6 Due 
4Nov 
Chapter 8 Cluster Analysis 

Nov 13th 
Data Mining Camp 

8th Week 


10Nov 
Chapter 8 Cluster Analysis 
HW #6 Due 
11Nov 
Papers 

9th Week 


17Nov 
Papers Chapter 9 

18Nov 
Chapter 9 

10th Week 

1Dec 
Chapter 10 
HW # 7 Due 
2Dec 
Chapter 10 




Topics for the Second Course (Winter 2010):
1. Logistic Regression
2. Markov Decision Process
3. Advanced Regression
LARS, Elastic Net, Generalised Linear Model, Generalised Additive Model
4. Trees
Regularisation, Ensemble Methods, MART, Boosting, Bagging, Random Forest
5. SVM
Regression
6. Expectation Maximisation
7. Principal Component Analysis
There are more Machine Learning References on my web site http://patriciahoffmanphd.com/
If you are in the Fall Class, please fill out the form
https://spreadsheets.google.com/embeddedform?formkey=dFVJbHZkVURWeVhqbFl2OTdhZ0JxNEE6MQ
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