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. 28, 2012     Project 1 posted; late policy for projects added to syllabus

Jan. 23, 2012     revised schedule posted

Jan. 15, 2012     added link to GradeBook for course

Jan. 9, 2012       links to Exercises 2 and slides for Lecture 2 added to schedule

Jan. 7, 2012       ATTENTION, Masters students!!  I discovered the class list I was given does not include CSS590 enrollees.  For the time being, this will prevent you from accessing the course Collect It and GoPost.  Please plan to turn in your answers to Exercises 1 by email at the address above, unless you hear otherwise.  I hope to get this resolved on Monday, Jan. 9.

Jan. 6, 2012       added link to GoPost discussion board for course

Dec. 29, 2011    updated schedule, with reading assignments and link to slides for first lecture

Dec. 27, 2011    faculty website put up for instructor

Dec. 19, 2011    draft syllabus posted, with tentative course schedule