*Patricia Hoffman and Mike Bowles teach a set of Machine Learning / Data Mining classes using R starting in January, in Silicon Valley.*

Mike Bowles and I are teaching a set of Machine Learning / Data Mining classes using R starting in January. We have been teaching these classes for about a year at the Hacker Dojo. At the bottom of this email are links to the different classes and more details about them.

People can sign up for the beginning class (Machine Learning 101) here:

www.meetup.com/HackerDojo-Cloud-Computing/calendar/15431990/

and they can sign up for the intermediate class (Machine Learning 201) here:

www.meetup.com/HackerDojo-Cloud-Computing/calendar/15493171/

**Future Machine Learning Classes**

**
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

**
Machine Learning 201:** Advanced Regression Techniques, Generalized Linear Models, and Generalized Additive Models

**Machine Learning 202:** Collaborative Filtering, Bayesian Belief Networks, and Advanced Trees

**Syllabus: **

**Machine Learning 101 1/22/2011 - 2/26/2011 ** Class Web Page

Week 1: Introduction to R, R memory model, R plots, Sampling, Distributions

Week 2: Supervised Classification and Prediction, Bias Variance Tradeoff

Week 3: Simple Regression, Regularization, Ridge Regression

Week 4: k Nearest Neighbors, Bayes Classifiers

Week 5: Support Vector Machines

**Machine Learning 102 3/5/2011 - 4/9/2011 ** Class Web Page

Week 1: Ensemble Methods book

Week 2: Cluster Analysis Unsupervised Learning, Agglomerative Clustering

Week 3: Discriminate Analysis, Expectation-Maximization Algorithms

Week 4: Anomaly Detection

Week 5: Students read papers in groups, group presentations summarizing papers with demo (working code if possible)

**Machine Learning 201 1/12/2011 - 2/10/2011 Class Web Site **

Week 1: Advanced Regression, L1 Regularized Regression

Week 2: Logistic Regression

Week 3: Subset Selections, Factors

Week 4: Generalized Linear Model

Week 5: Generalized Additive Model

**Machine Learning 202 2/16/2011 - 3/10/2011 Class Web Site **

Week 1: Collaborative Filtering, Recommendation Engines

Week 2: Bayesian Belief Networks, EM & Factor Analysis

Week 3: Advanced Trees

Week 4: Gradient Boosting

Week 5: Learning Theory, Debugging Methods