Maching Learning in Python - Unsupervised learning and model validation

Overview: This workshop will focus on unsupervised machine learning and model validation. Unsupervised machine learning is a powerful technique where the algorithm analyzes and clusters unlabeled datasets. This workshop will scratch the surface of this side of machine learning, introducing unsupervised learning using the k-means and DBSCAN algorithms. This session will explore the model validation process in the machine learning pipeline in more detail.

By the end of the workshop, participants will be able to:

• Differentiate between supervised and unsupervised learning
• Given a scaffolded environment and curated data set, train a DBSCAN model and describe how this algorithm works at a high level
• Articulate techniques used for model validation

Prereqs: Participants should already have some familiarity with Python programming fundamentals, e.g. loops, conditional execution, importing modules, and calling functions. Furthermore, participants should ideally have attended the first lesson in the “Fundamentals of Machine Learning in Python” series, or they should already have some background on the general machine learning pipeline.

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).
Instructor: Jacob Errington, Faculty Lecturer in Computer Science at McGill University. Eric Mayhew, Computer Science professor at Dawson College.