Advanced Machine Learning and Data Mining
Statistical aspects of supervised learning: regression, regularization methods, parametric and nonparametric classification methods, including Gaussian processes for regression and support vector machines for classification, model averaging, model selection, and mixture models for unsupervised learning. Some advanced methods will include Bayesian networks and graphical models.
Time, Location, Etc.
Date/Time: Fall 2016, Wednesdays 12pm–3pm, starting September 7, ending November 30.
Room: IC 328
The assignments are to be done by each student individually. You may discuss the general idea of the questions with anyone you like, but your discussion may not include the specific answers to any of the problems.
Late Assignments will be penalized 10% of the available marks per day up to a maximum of three days. Beyond this, no extensions will be granted on homework assignments, except in the case of an official Student Medical Certificate or a written (not emailed) request submitted at least one week before the due date and approved by the instructor. Please plan ahead.
As a general rule, small matters of marking on assignments (apparent errors, questions about evaluation criteria, etc.) should be taken first to the marker (via email). More significant issues, or unresolved matters on assignments, are appropriate to take to the professor. Matters of marking on tests and exams should be taken to the professor.