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on September 28, 2010 at 3:36:16 pm
 

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.r-project.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 

 

Upcoming Machine Learning Events

 

General Calendar for the Year:

 

Fall 2010: Basic Machine Learning  

Book:  Introduction to Data Mining by Pang-Ning 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  
 
22-Sep Chapter 1 & 2    Notes For First Week
Papers
23-Sep Chapter 3  
2nd Week  
 
29-Sep Chapter 4           Notes for 2nd Week
HW # 1  HW1.pdf  Due
30-Sep Chapter 4   
3rd Week  
 
6-Oct Simple Regression HW #2 Due
7-Oct Ridge Regression  
4rd Week  
 
13-Oct Chapter 5 HW #3 Due
14-Oct Chapter 5  
5th Week  
 
20-Oct Chapter 5 HW #4 Due
21-Oct Chapter 5  
6th Week  
 
27-Oct Chapter 5 HW #5 Due
28-Oct Chapter 6  
7th Week  
 
3-Nov Chapter 6 HW #6 Due
4-Nov Chapter 8 Cluster Analysis  
Nov 13th Data Mining Camp  
8th Week  
 
10-Nov Chapter 8 Cluster Analysis HW #6 Due
11-Nov Papers  
9th Week  
 
17-Nov Papers Chapter 9  
18-Nov Chapter 9  
10th Week  
1-Dec Chapter 10 HW # 7 Due
2-Dec 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

 

 

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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