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.rproject.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.
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/kaggleinclasslauncheswithstanfordstats202/
Interesting Competition
General Sequence of Classes:
Beginning Applied Machine Learning
Text: "Introduction to Data Mining", by PangNing 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:
Week 
Topics 
Homework 
Links 




1st Week 
Ensemble Methods & More 


3/5/2011 
Ensemble Methods 



Bias  Variance Decomposition 



Class Imbalance 










2nd Week 
Cluster Analysis  Basic 


3/12/2011 
kmeans 
HW #1 Due 


Hierarchical & Density Clustering 










3rd Week 
Cluster Analysis  Algorithms



3/19/2011 
EM Algorithms 
HW #2 Due 


Discriminate Analysis 










4th Week 
Anomaly Detection



4/2/2011 

HW #3 Due 









5th Week 
Special Topics 


4/9/2011 
Class Presentations 
Papers 









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 Sequence:
"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 my web site http://patriciahoffmanphd.com/
If you are in the Winter Class, and have not already done so, please fill out the form: Register for Class
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