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Machine Learning 102 meets from 9 AM to 1:00 PM and lasts for 5 Saturdays: March 12, 19 and April 9, 16, 23

Cost is $200/5 week session for Hacker Dojo members and $300.00 per 5 week session for non members. The Machine Learning 101 class was very full so sign up early for Machine Learning 102. Please pay your money on Eventbright ( Your payment on Eventbright will secure your spot in the class.

Overview of the course
Machine Learning 102 will cover unsupervised learning and fault detection.

Both 101 and 102 begin at the level of elementary probability and statistics and from that background survey a broad array of machine learning techniques. The classes will give participants a working knowledge of these techniques and will leave them prepared to apply those techniques to real problems. To get the most out of the class, participants will need to work through the homework assignments.

This class assumes a moderate level of computer programming proficiency. We will use R (the open source statistics language) for the homework and for the examples in class. We will cover some of the basics of R and do not assume any prior knowledge of R. You can find references to how to use R on this website and we will give out sample code during classes that will help get you started.

You'll need some general beginner-level background in probability, calculus, linear algebra and vector calculus. We will cover most of what is required during the lectures. The appendices in the back of the text Introduction to Data Mining by Tan et. al. are more than sufficient level for this class.

Machine Learning 101 and 102 can be taken in any any order. The prerequisites for the two classes are the same. Machine Learning 102 will culminate in the students giving presentations on papers they have read.

The Web Page for Machine Learning 101 (

The Web Page for Machine Learning 102 (

Past class ( web page

Book ( the class will loosely follow

Please fill out the class survey ( if you are going to sign up for the class.

Machine Learning 102 3/12/2011 - 4/23/2011 Class Web Page (
Week 1: Ensemble Methods
Week 2: Cluster Analysis Unsupervised Learning, Agglomerative Clustering
Week 3: Discriminate Analysis, Expectation-Maximization Algorithms
Week 4: Chapter 10 Anomaly Detection
Week 5: Students read papers in groups, group presentations summarizing papers with demo (working code if possible)