Machine Learning in Python - Neural networks part 2 & AI Ethics

Overview: In this lesson, we will redo the example of recognizing handwritten digits, but this time using a different kind of neural network more suited to image recognition tasks – convolutional neural networks. We will mention a few other forms of neural networks and their typical use cases. Finally, since neural networks are extraordinary tools, we will discuss the responsibility that we as ML practitioners have to use them ethically. We will look at situations where AI falls short of its promises, sometimes in spectacularly funny ways, and other times in devastatingly sad ways.

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

• Critique the use of AI in a social context.
• Enumerate the different kinds of neural networks together with their associated use cases.
• Given a scaffolded environment and curated data set, follow a tutorial that trains a convolutional neural network to perform classification.

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.