Welcome Hacker Dojo

Modern Applied Machine Learning 201

Organizer: Doug Chang

Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman

This is an advanced Hands On Data Mining and Machine Learning class. 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 Stats 202 as a prerequisite. (If you have taken our Machine Learning 101 and 102 classes, you are well prepared for this course.)  

We've put together three sequences of classes.  This is the second sequence.  Our objective for students in Machine Learning 201 is to understand advanced regression techniques in detail.  Both Generalized Linear Models and Generalized Additive Models will be addressed. You should be able to extend or modify these methods to suit the needs of your particular problems.  The second five week session (Machine Learning 202) will culminate in the students giving presentations on papers they have read.   You may start with Machine Learning 201 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 session, we expect you can use R at a beginning level. For your review, R are here: References for R,  Reference for R Comments,  More R references.  To integrate R with Eclipse click here.

Easy access to The Google Group 

 Click here for  Upcoming Machine Learning Events

Compete with Stanford's Class - We can win it! http://kaggle.com/blog/2010/11/08/kaggle-in-class-launches-with-stanford-stats-202/

Interesting Competition

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

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:  

Extended Machine Learning Project (Competition)

Machine Learning 400:

Machine Learning 201 Syllabus:  

General Calendar for the Year:

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

Winter  2011:  Machine Learning 101 &  Machine Learning 201

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 Fall Class, please fill out the form

https://spreadsheets.google.com/embeddedform?formkey=dFVJbHZkVURWeVhqbFl2OTdhZ0JxNEE6MQ

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Welcome Hacker Dojo Machine Learning Class 202 - Modern Applied Machine Learning Second Session

General Sequence of Classes:

Beginning Applied Machine Learning

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

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:  

Extended Machine Learning Project (Competition)

Machine Learning 400: