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Welcome to Machine Learning II of the MSBA program at Mason School of Business, College of William & Mary!

This is the second of two courses designed to equip students with the kinds of analytical skills used in the era of Big Data to reveal the hidden patterns in, and relationships among, data elements being created by internal transaction systems, social media and the Internet of Things. This second machine learning course covers many methodologies including various non-linear approaches, tree-based methods, support vector machines, principal components analysis, and unsupervised machine learning techniques. The R language is used extensively in this course.
  • An Introduction to Statistical Learning, with Applications in R (a.k.a. ISLR) by James, Witten, Hastie and Tibshiran is a required textbook for the course. A free PDF version of the textbook, PowerPoint slides and videos from the textbook authors are available on this site.
  • Powerpoint slides, code files, additional readings or video materials will be assigned and posted by the course instructor on this external course website that you're currently visiting.
  • The William & Mary Blackboard site for this course will be used for the syllabus, assignment submissions, course announcements, and grading purposes.
  • Students should stay up with the field by following key publications in this field, such as Harvard Data Science Review, and R-Bloggers.
Note: Copyright of these course materials belongs to the course instructor. Posting or sharing course materials online or otherwise publicly without the instructor’s permission may constitute copyright violation and will be reported to the Honors Council.

This course follows the MSBA Program Calendar. For more information about the MSBA program, please visit the myMSBA site.

This course is designed based on Dr. David Murray's version of the same course.