| 
  • If you are citizen of an European Union member nation, you may not use this service unless you are at least 16 years old.

  • Whenever you search in PBworks, Dokkio Sidebar (from the makers of PBworks) will run the same search in your Drive, Dropbox, OneDrive, Gmail, and Slack. Now you can find what you're looking for wherever it lives. Try Dokkio Sidebar for free.

View
 

FrontPage

This version was saved 11 years, 2 months ago View current version     Page history
Saved by mike@mbowles.com
on September 21, 2011 at 10:46:36 am
 

 

Machine Learning 202

Organizer: Doug Chang

Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman

 

We will cover several machine learning topics in detail.  Among those are Bayesian Belief Networks, Prof. Jerome Friedman's gradient boosting papers, svd's and recommender systems. 

 

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 "Introduction to Data Mining" by Tan.  If you have taken our Machine Learning 101102 classes and Machine Learning 201, you are well prepared for this course.

 

We expect you to have previously used R.  We will use R for discussing homework problems and comparing different solution approaches.  .   http://cran.r-project.org/  For your review, R are here: References for R,  Reference for R Comments,  More R references.  To integrate R with Eclipse click here.

 

To join the Class Notifications Email list please fill out this form.

 

Machine Learning 202 Syllabus:  

 

Week  Topics  Homework  Links 
       
1st Week  Collaborative Filtering HW01
RecommenderLinks
     8/24/2011 Singular Value Decomposition    
     8/25/2011  Recommendation Engines     
       
2nd Week       Gradient Boosting
  GradientBoostingLinks  
    8/31/2011 Analysis of AdaBoost
HW #1 Due   
    9/01/2011  Friedman's Stochastic Gradient Boosting
GradBoostHW 
 
   
   
3rd Week  Student Topics
 
 
    9/7/2011     Active Learning
HW #2 Due  ActiveLearningLinks
    9/8/2011   Learning Theory  & AdaBoost
 
AdaBoost  
       
4th Week  More Advanced Trees
   
    9/14/2011    HW #3 Due  EMLinks  
    9/15/2011       
       
5th Week  Special Topics    Week5Notes  
    9/21/2011 Class Presentations  &  Debugging Methods  Papers  Papers for Presentation  
    9/22/2011  Class Presentation  &  Learning Theory
   
       

 

General Sequence of Classes:

 

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

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

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

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

 

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

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

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

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

 

Machine Learning Big Data:  Adaptation and execution of machine learning algorithms in the map reduce framework.

 

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

 

We will be using the following text as a reference for 201 and 202:

 

"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 Patricia's web site http://patriciahoffmanphd.com/

 

Be sure to sign up on the meetup page.

 

Comments (0)

You don't have permission to comment on this page.