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 More R references
Easy access to The Google Group
Click here for Upcoming Machine Learning Events
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 Notes For First Week

Papers 
23Sep 
Chapter 3 

2nd Week 


29Sep 
Chapter 4 Notes for 2nd Week

HW #1 Due
HomeworkAssignment01.doc
HW # 1 HW1.pdf

30Sep 
Chapter 4 

3rd Week 


6Oct 
Simple Regression Notes for Week 03 
HW # 2 Due
Homework02.doc
Homework02.pdf

7Oct 
Ridge Regression & k Nearest Neighbors


4rd Week 


13Oct 
Chapter 5 Week04
finish k Nearest Neighbors
Naive Bayes

HW #3 Due
Homework03.doc Homework03.pdf

14Oct 
Chapter 5
Support Vector Machines


5th Week 


20Oct 
Chapter 5
finish SVM
Start Ensemble Methods

HW #4 Due
Homework04.doc
Homework04.pdf

21Oct 
Chapter 5
Finish Ensemble Methods


6th Week 


27Oct 
Chapter 5
Class Imbalance

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 




Lectures are in the Lectures Folder
Homeworks are in the Homework Folder
DataFiles
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
We will be using the following text as a reference for the Second Course:
"The Elements of Statistical Learning  Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
This book is free to look at on line. http://wwwstat.stanford.edu/~tibs/ElemStatLearn
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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