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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