This is an introductory grad course on machine learning. Machine learning is a set of techniques that allow machines to learn from data and experience, rather than requiring humans to specify the desired behavior by hand. Over the past two decades, machine learning techniques have become increasingly central both in AI as an academic field, and in the technology industry. This course provides a broad introduction to some of the most commonly used ML algorithms.

The first half of the course focuses on supervised learning. We begin with nearest neighbours, decision trees, and ensembles. Then we introduce parametric models, including linear regression, logistic and softmax regression, and neural networks. We then move on to unsupervised learning, focusing in particular on probabilistic models, but also principal components analysis and K-means. Finally, we cover the basics of reinforcement learning.

We will have a single section, taught on Fridays 1-3 PM EST. Lectures will be delivered online synchronously and students will have the opportunity to ask questions and participate. Recordings for those unable to attend will be posted internally on MyMedia. Tutorials and Q/A office hours will be delivered online synchronously. Tutorials will be held at Tuesdays at 10am and 10pm (also recorded and uploaded) so that it'll work for every time zone. The same material will be presented at both tutorial times.

Where and When

Lecture and tutorials will be held online. A link will be provided through Quercus.

Lecture Time Tutorial Time Lecture/Tutorial "Room" Start End
Fridays, 1-3 PM EST Tuesdays 10am and 10pm Online, find details on Quercus Jan. 15 Apr. 9



Prerequisites (an undergrad course in each is sufficient):

Lateness. Homeworks will be accepted up to 3 days late, but 10% will be deducted for each day late, rounded up to the nearest day.

Remarks. Remark requests for homeworks should be made through MarkUS, and will be considered by the same TA who marked the assignment. The deadline for requesting a remark is typically one week after the marked assignments are returned. Remark requests for exams will be handled by the instructor; details to be announced later.

Computing Resources

For the homework assignments, we will use Python, and libraries such as NumPy, SciPy, and scikit-learn. We will use Python 3. You have two options: