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on December 3, 2010 at 3:40:38 pm

Hacker Dojo Machine Learning Class 101 & 102 Beginning Applied Learning


Organizer: Doug Chang

Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman


We've put together three sequences of classes.  Our objective for the second sequence is to enable you to read the current literature, to implement algorithms based on what you read and to extend or modify methods in the current machine learning oeuvre in order to suit the needs of your particular problems.  The first sequence will cover basic techniques in ML at sufficient depth that you will be able to immediately apply the techniques you've learned to real problems.  The first session (Machine Learning 101) in this sequence covered basic techniques in ML at sufficient depth that you will be able to immediately apply the techniques you've learned to real problems.   This second five week session (Machine Learning 102) will culminate in the students giving presentations on papers they have read.   


We're going to use R as our lingua franca for looking at homework problems, discussing them and comparing different solution approaches.    Load R onto your laptop or desk computer before you come to the first class.   http://cran.r-project.org/  We will include some descriptive material on using R in the first two lectures in order to get everyone up to speed on it. References for R are here: References for R Comment on these references here:  Reference for R Comments  More R references


General Calendar for the Year:


Fall 2010: Basic Machine Learning  

Book:  Introduction to Data Mining by Pang-Ning Tan et al


Winter  2010:  Advanced Machine Learning

Books:  Professor Andrew Ng's lecture notes from CS229 and Elements of Statistical Learning, Hastie et al


Spring 2011:   Extended Machine Learning Project (Competition)



Complete Outline for the first Course (Fall 2010):  


                                                                            Machine Learning 101



1st Week  
22-Sep Chapter 1 & 2                                       Notes For First Week
23-Sep Chapter 3  
2nd Week  
29-Sep Chapter 4                                                Notes for 2nd Week

HW #1 Due


HW # 1  HW1.pdf

30-Sep Chapter 4   
3rd Week  
6-Oct Simple Regression                                 Notes for  Week 03

HW # 2 Due



7-Oct Ridge Regression & k Nearest Neighbors
4rd Week  

Chapter 5                                                                 Week04

finish k Nearest Neighbors

Naive Bayes

HW #3 Due

Homework03.doc Homework03.pdf


Chapter 5

Support Vector Machines

5th Week  

Chapter 5 Week05

finish SVM

Start Ensemble Methods

HW #4 Due





Chapter 5

Finish Ensemble Methods

  Machine Learning 102  
6th Week  

Chapter 5  Week06

Class Imbalance

HW #5 Due

Homework05.doc  Homework05.pdf

28-Oct Chapter 6  
7th Week  
3-Nov Chapter 8 Week07

HW #6 Due

Homework06.doc  Homework06.pdf

4-Nov Chapter 8 Cluster Analysis  
8th Week  

Papers Week08

Group 3, Group 4

Work Hard on your Presentations
11-Nov Papers  Group 1, Group 2  
13-Nov    Data Mining Camp Saturday Instructions 
9th Week  
17-Nov Chapter 9 Week09  HW #7 on Chapter 8  Due
18-Nov Chapter 9  
10th Week  
1-Dec Chapter 10, Week10  HW #8 on Chapter 9 Due
2-Dec Chapter 10  


Lectures are in the Lectures Folder

Homeworks are in the Homework Folder



There are more Machine Learning References on my web site http://patriciahoffmanphd.com/



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 Tibshirani, and Jerome Friedman

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:

     Data Mining Social Networks

     Text Mining

     Recommender Methods

     Big Data


Extended Machine Learning Project (Competition)

Machine Learning 400:




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