# Welcome Hacker Dojo Machine Learning Class Fall 2010

Organizer: Doug Chang

Instructors: Dr. Michael Bowles & Dr. Patricia Hoffman

We've put together a calendar for the next three machine learning courses that we're going to teach. Our objective for the 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 course in this 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.

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

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 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):**

Lectures are in the Lectures Folder

Homeworks are in the Homework Folder

DataFiles

**Topics for the Second Course (Winter 2010):**

1. Logistic Regression

2. Markov Decision Process

3. Advanced Regression

-LARS, Elastic Net, Generalised Linear Model, Generalised Additive Model

4. Trees

-Regularisation, Ensemble Methods, MART, Boosting, Bagging, Random Forest

5. SVM

-Regression

6. Expectation Maximisation

7. Principal Component Analysis

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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