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on September 19, 2010 at 9:59:51 am
 

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.  Here's an annotated list of references on R, to help you get started. 

 

 

The general Calendar for the year:

 

 

Fall 2010         Basic Machine Learning  

                      Introduction to Data Mining by Pang-Ning Tan et al

Winter  2010   Advanced Machine Learning

                     Elements of Statistical Learning Hastie et al

Spring 2011    Extended Machine Learning Project (Competition)

 

 

Complete Outline for the first Course:

 

 

1st Week  
 
22-Sep Chapter 1 & 2 Papers
23-Sep Chapter 3  
2nd Week  
 
29-Sep Chapter 4  HW # 1 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  
 
 
 

 

 

 

Topics for the Second Course:

 

Logistic Regression

Markov Decision Process

Advanced Regression

      LARS, Elastic Net, Generalized Linear Model, Generalized Additive Model

Trees

     Regularization, Ensemble Methods, MART, Boosting, Bagging, Random Forest

SVM

     Regression

Expectation Maximization

Principal Component Analysis

 

 

 

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