Machine Learning in Python - ML overview and k-nearest neighbours algorithm

Overview: Nowadays, machine learning (ML) is perhaps the hottest topic in all Computer Science, and with good reason: the variety of tasks that can be completed by machine learning models has exploded in the last 15 years as compute power has reached new heights. But what exactly is a “machine learning model”? This workshop will introduce you to the basic terminology and concepts associated with machine learning in a hands-on way. We will explore common ML tasks such as data acquisition and cleaning as well as model training, testing, and validation by focusing on a particularly simple kind of model called k-nearest neighbours.

Learning Goal(s): By the end of the workshop, participants will be able to:

• Describe at a high level what machine learning is and how it works, the uses and applications of machine learning, as well as its limitations and ethical considerations.
• Describe the machine learning pipeline, consisting of data acquisition, data cleaning, algorithm selection, training, testing, and validation.
• Explain in plain English how the following algorithm works: k-nearest neighbours

Prereqs: Participants should already have some familiarity with Python programming fundamentals, e.g. loops, conditional execution, importing modules, and calling functions.

Approach: Our approach is primarily student-centered. Students will work in pairs and small groups on worksheets and Jupyter notebooks, interspersed with brief lecture and instructor-led live-coding segments.

Supporting Resources: We will refer to many of the materials used previously in our series “Computing Workshop” https://computing-workshop.com/

Deliverables: Our resources will be made available via our web site.

Resources required: Participants should have access to a laptop computer. Python should be already installed with Anaconda.

Location: HYBRID. Online via Zoom, or in-person at Burnside Hall room 1104 (11th floor).
Instructors: Jacob Errington, Faculty Lecturer in Computer Science at McGill University. Eric Mayhew, Computer Science professor at Dawson College.