COMP-551 (Fall 2017): Topics in Computer Science: Applied Machine Learning

INSTRUCTOR: Joelle Pineau
TAs: T.b.d.

Term: Fall 2017.
When: Mondays, 1-2:30pm, Leacock 26.
When: Wednesdays, 8:30-10am, MC304.

Important announcements:

Useful links:


The course will cover selected topics and new developments in data mining and applied machine learning, with a particular emphasis on good methods and practices for effective deployment of real systems. We will study commonly used algorithms and techniques, including linear and logistic regression, clustering, neural networks, support vector machines, decision trees and more. We will also discuss methods to address practical issues such as feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large datasets. Important note:Students who took COMP-652 in 2013 or before CANNOT take COMP-551. Students who took COMP-652 in Winter 2014 or after (or intend to take it) can take COMP-551. Contents of both courses have been designed to avoid too much overlap. COMP-551 focuses on the practical application of machine learning, whereas COMP-652 (starting in Winter 2014) focuses on theoretical analysis of machine learning, reinforcement learning, bandits and analysis of time series.

List of topics (subject to minor changes):

IMPORTANT: The schedule is subject to change. Up-to-date information about the schedule and assigned readings will be posted on the class web page.