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on December 7, 2010 at 6:08:51 pm
 

Welcome Hacker Dojo

 

Modern Applied Machine Learning 202

 

 

Organizer: Doug Chang

Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman

 

For Machine Learning 202, we assume you are familiar with basic statistical concepts and have the ability to write programs running different algorithms on public data sets. We assume knowledge at the level of Stanford's Stats 202 class as a prerequisite. (If you have taken our Machine Learning 101102 classes and Machine Learning 201, you are well prepared for this course.)  

 

Our objective for students in Machine Learning 202 is to understand advanced regression techniques in detail.  Machine Learning 202 will culminate in the students giving presentations on papers they have read.   You may start with Machine Learning 202 without taking the Machine Learning one hundred level sequence, as long as you are familiar with and have programmed some of the data mining techniques covered in that sequence.

 

We are continuing to use R as our lingua franca for looking at homework problems, discussing them and comparing different solution approaches.   You should have previously loaded R onto your laptop or desk computer before you come to the first class.   http://cran.r-project.org/  As this is the second sequence, we expect you have previously used R.  For your review, R are here: References for R,  Reference for R Comments,  More R references.  To integrate R with Eclipse click here.

 

 

 Click here for  Upcoming Machine Learning Events

 

General Sequence of Classes:

 

Beginning Applied Machine Learning

     Text: "Introduction to Data Mining", by Pang-Ning Tan, Michael Steinbach and Vipin Kumar

Machine Learning 101:   Learn about ML algorithms and implement them in r  

Machine Learning 102:  Enable you to read and implement algorithms from current papers

 

Modern Applied Machine Learning

     Text:  "The Elements of Statistical Learning - Data Mining, Inference, and Prediction"  by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Machine Learning 201:    Advanced Regression Techniques, Generalized Linear Models, and Generalized Additive Models    

Machine Learning 202:   Collaborative Filtering, Bayesian Belief Networks, and Advanced Trees

 

Advanced Topics

Machine Learning 300 series:  

     Data Mining Social Networks

     Text Mining

     Recommender Methods

     Big Data

 

Extended Machine Learning Project (Competition)

Machine Learning 400:

 

Machine Learning 202 Syllabus:  

 

Week  Topics  Homework  Links 
       
1st Week  Collaborative Filtering    
     2/16/2011 Singular Value Decomposition    
     2/17/2011  Recommendation Engines     
       
       
2nd Week       Basic Bayesian Belief Networks     
    2/23/2011 EM & Factor Analysis
HW #1 Due   
    2/24/2011       
       
   
   
3rd Week  Advanced Trees
 
 
    3/2/2011     Gradient Boosting
HW #2 Due  
    3/3/2011   Learning Theory   
 
       
       
4th Week  More Advanced Trees
   
    3/9/2011    HW #3 Due   
    3/10/2011       
       
       
5th Week  Special Topics     
    3/16/2011 Class Presentations  &  Debugging Methods  Papers   
    3/17/2011  Class Presentation  &  Learning Theory
   
       
       

 

General Calendar for the Year:

 

Fall 2010: Basic Machine Learning Machine Learning 101 &  Machine Learning 102

 

Winter  2011:  Machine Learning 101 &  Machine Learning 201

 

Early Spring 2011:  Machine Learning 102 &  Machine Learning 202

 

Lectures are in the Lectures Folder

Homeworks are in the Homework Folder

DataFiles

 

 

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 Winter Class, and have not already done so, please fill out the form:  Register for Class

 

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