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.
Machine Learning 202 Syllabus:
Week 
Topics 
Homework 
Links 




1st Week 
Background for Ensemble Methods




Trees for Classification and Regression


Trees 

Learning Theory and AdaBoost 

AdaBoost 




2nd Week 
Gradient Boosting


GradientBoostingLinks 

Analysis of AdaBoost




Friedman's Stochastic Gradient Boosting







3rd Week 
Random Forest and Performance Comparisons 

RandomForests_PerfComparison


Breiman's Random Forest Algorithm 



Comparison of Machine Learning Algo's







4th Week 
Reduced Rank Matrix Approx and Active Learning




Reduced Rank Matrix Approximation (Recommender by ALS) 

ReducedRankApprox


Active Learning

HW #2 Due 
ActiveLearningLinks





Other Topics





Recommender Systems




Basic Background

HW #3 Due 
EMLinks 

EM for Gaussian Mixture Models








More EM Applications


RecommenderLinks 

LDA I


LDA


Hidden Markov Models


hmm 





Learning Theory & AdaBoost


AdaBoost 




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 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
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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