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 101, 102 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.rproject.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 
Active Learning & AdaBoost



9/7/2011 
Active Learning

HW #2 Due 
ActiveLearningLinks

9/8/2011 
Learning Theory & AdaBoost


AdaBoost 




4th Week 
EM Method



9/14/2011 
Basic Background

HW #3 Due 
EMLinks 
9/15/2011 
EM for Gaussian Mixture Models







5th Week 
More EM Applications



9/21/2011 
LDA I


LDA

9/22/2011 
Hidden Markov Models


hmm 




General Sequence of Classes:
Machine Learning 101: Learn about ML algorithms and implement them in r
Text: "Introduction to Data Mining", by PangNing 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 PangNing 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.
Machine Learning Big Data: Adaptation and execution of machine learning algorithms in the map reduce framework
Machine Learning Text Processing: Machine learning applied to natural language text documents using statistical algorithms including indexing, automatic classification (e.g. spam filtering) part of speech identification, topic and modeling, sentiment extraction
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://wwwstat.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.
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