Course description

Machine learning is the science of building predictive models from available data, in order to predict the behavior of new, previously unseen data. It lies at the intersection of modern statistics and computer science, and is widely and successfully used in medicine, image recognition, finance, e-commerce, textual analysis, and many areas of scientific research, especially computational biology. This course is an introduction to the theory and practical use of the most commonly used machine learning techniques, including decision trees, logistic regression, discriminant analysis, neural networks, naïve Bayes, k-nearest neighbor, support vector machines, collaborative filtering, clustering, and ensembles. The coursework will emphasize hands-on experience applying specific techniques to real-world datasets, combined with several programming projects.

Announcements (most recent first)

Jan. 14, 2014   I still do not have access to Truly House.  Office hours today will be held from 5:00-6:30 in Common Grounds, the dining/lounge area on the ground floor of UW2.

Jan. 9, 2014     Links to Catalyst tools and information on MATLAB licenses added to sidebar of main course website.  Exercises 2 posted.

Jan. 7, 2014     Slides for Lecture 2 and Exercises 1 posted.

Jan. 6, 2014     Syllabus with preliminary schedule of lecture topics posted to course website.  Slides for Lecture 1 are linked from the schedule.  Please ignore last column in schedule for now - it has not been updated.