Machine Learning in Python - Decision Trees & Data Acquisition

Overview: Decision trees model sequential decision-making and can be used for both classification and regression tasks. This workshop will introduce you to this type of machine learning model in a hands-on way: you will train a decision tree on a given dataset. We will discuss the pros and cons of decision trees, and see how using a random forest (a collection of decision trees) helps to address some of their shortcomings.

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

• Describe with a diagram what a decision tree is.
• Describe the advantages and disadvantages of decision trees as a machine learning algorithm.
• Train a decision tree on a given data set for use in classification tasks, following guidance from instructors.
• Describe the data acquisition process in machine learning and its ethical considerations

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.