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on November 15, 2010 at 10:18:55 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 


 Click here for  Upcoming Machine Learning Events


Compete with Stanford's Class - We can win it! http://kaggle.com/blog/2010/11/08/kaggle-in-class-launches-with-stanford-stats-202/


Interesting Competition


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
23-Sep Chapter 3  
2nd Week  
29-Sep Chapter 4                                                Notes for 2nd Week

HW #1 Due


HW # 1  HW1.pdf

30-Sep Chapter 4   
3rd Week  
6-Oct Simple Regression                                 Notes for  Week 03

HW # 2 Due



7-Oct Ridge Regression & k Nearest Neighbors
4rd Week  

Chapter 5                                                                 Week04

finish k Nearest Neighbors

Naive Bayes

HW #3 Due

Homework03.doc Homework03.pdf


Chapter 5

Support Vector Machines

5th Week  

Chapter 5 Week05

finish SVM

Start Ensemble Methods

HW #4 Due





Chapter 5

Finish Ensemble Methods

6th Week  

Chapter 5  Week06

Class Imbalance

HW #5 Due

Homework05.doc  Homework05.pdf

28-Oct Chapter 6  
7th Week  
3-Nov Chapter 8 Week07

HW #6 Due

Homework06.doc  Homework06.pdf

4-Nov Chapter 8 Cluster Analysis  
8th Week  
10-Nov Papers Group 3, Group 4 Work Hard on your Presentations
11-Nov Papers  Group 1, Group 2  
13-Nov    Data Mining Camp Saturday Instructions 
9th Week  
17-Nov Chapter 9 Week09  HW #7 on Chapter 8  Due
18-Nov Chapter 9  
10th Week  
1-Dec Chapter 10  
2-Dec Chapter 10  


Lectures are in the Lectures Folder

Homeworks are in the Homework Folder



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


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://www-stat.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



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