Machine Learning 201

Instructor Dr. Michael Bowles

THIS COURSE MEETS Sat morning 9am till 1pm

Dates we'll meet: Apr 7, Apr 14, Apr 28, May 5, May12 (Notice: not meeting Apr 21)

To register go to http://ml201.eventbrite.com

Overview of the Course

Machine Learning 201 begins with ordinary least squares regression and extends this basic tool in a number of directions. We'll consider various regularization... [read more]

Instructor Dr. Michael Bowles

THIS COURSE MEETS Sat morning 9am till 1pm

Dates we'll meet: Apr 7, Apr 14, Apr 28, May 5, May12 (Notice: not meeting Apr 21)

To register go to http://ml201.eventbrite.com

Overview of the Course

Machine Learning 201 begins with ordinary least squares regression and extends this basic tool in a number of directions. We'll consider various regularization approaches. We'll introduce logistic regression and we'll learn how to code categorical inputs and outputs. We'll look at feature space expansions. We'll cover coefficient shrinkage methods and modern algorithms for calculating coefficient paths.

Text: "The Elements of Statistical Learning - Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Machine Learning 201 Syllabus:

Week Topics

1st Week Regularized Regression - Subset Selection, etc.

2nd Week Coefficient Shrinkage Methods

3rd Week Factor Inputs/Outputs

4th Week Generalized Linear Models

5th Week Logistic and Multi-Factor Regression - Glmnet

For more detail on the schedule visit: http://machinelearning201.pbworks.com/w/page/32890379/FrontPage

Prerequisites

Machine Learning 201 and 202 employ beginner-level probability, calculus and linear algebra (e.g. preruse the appendices in "Introduction to Data Mining" by Tan et. al. or Linear Algebra, and Probability Theory.) If you have taken Machine Learning 101 and 102 classes, you are well prepared for this course, but those are not required to start 201.

Participants should be familiar with R or be willing to pick R up outside of class. If there's interest, I'll do a tutorial on using R as a separate class. You'll receive R-code for most of the examples. Come to the first class with R loaded on your computer. http://cran.r-project.org/

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