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 "Introduction to Data Mining" by Tan.  If you have taken our Machine Learning 101, 102 classes and Machine Learning 201, you are well prepared for this course.

We intend to cover several machine learning topics in detail.  Among those are Bayesian Belief Networks and Prof. Jerome Friedman's gradient boosting papers.  You may start with Machine Learning 202 without taking the Machine Learning 101 and 102, as long as you are familiar with and have programmed some of the data mining techniques covered in that sequence.

We will use R for 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.

Machine Learning 202 Syllabus:  

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

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.