One of the most discussed and perhaps mysterious machine learning models is the neural network. Neural networks are a kind of machine learning model inspired by biological processes taking place in the brain. This lesson will demystify neural networks and provide you with a plain-English explanation of how they work. We will train a neural network to recognize handwritten digits; this is a classification task. We will discuss some variants on neural networks such as convolutional neural networks. We will also discuss deep learning and further explore the training step in the machine learning pipeline.

By the end of the workshop, participants will be able to:
- Given a scaffolded environment and curated data set, follow a tutorial that trains a neural network to perform classification;
- Describe in plain English what a neural network is and what deep learning is;
- Describe at a high-level what the training process is for neural networks and distinguish it from the training processes seen previously.

Prerequisites: 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.
      · You need to bring your own laptop for this workshop. Contact us if you would like to attend but it's impossible for you to bring a laptop.
      · Install Anaconda on your computer. You can find installation instructions here. Please contact us (cdsi.science@mcgill.ca) if you are having trouble with installation.

Supporting resources: Some materials that will be used are available at the instructors' website Computing Workshop.

Location: Hybrid. Online via Zoom, or in-person at Burnside Hall room 1104 (11th floor).
Instructors: Jacob Errington, Faculty Lecturer, and Eric Mayhew, graduate student, School of Computer Science, McGill University.