Description: This presentation examines the evolving intersection of AI, copyright, and research data management (RDM) in the Canadian context. It explores how generative AI challenges traditional concepts of authorship, ownership, and fair dealing, drawing on emerging legal cases, government consultations, and international developments. The session highlights risks to creators from unlicensed data scraping and style mimicry, and reviews emerging technical and policy responses. Finally, the presentation positions RDM as a critical foundation for accountability, data provenance, metadata integrity, and trusted research practices in an AI‑driven information ecosystem.
Teachers: Jeff Moon (Compute Ontario), Lucia Costanzo (University of Guelph)
Level: Introductory
Format: Lecture
Certificate: Attendance
Prerequisites:
- None
Description (Parts 1 and 2): Introduction of neural network programming concepts, theory and techniques. The class material will being at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate concepts.
Description (Part 3): This part will continue the development of the neural network programming approaches from Parts 1 & 2. This part will focus on methods used to generate sequences: LSTM networks, sequence-to-sequence networks, and transformers.
Teacher: Erik Spence (SciNet, University of Toronto)
Level: Introductory
Format: Lecture + Hands-on
Certificate: Attendance
Prerequisites:
- Experience with Python will be assumed. (This course is being taught assuming this.)
- No prior experience with the Keras neural framework is expected. (The Keras neural framework will be used for neural network programming.)
Description: This course provides an introduction to machine learning that enables computers to learn AI models from data without being explicitly programmed. It comprises two parts:
- Part I covers the fundamentals of machine learning, and
- Part II demonstrates the applications of various machine methods in solving a real world problem.
Rather than presenting the key concepts and components of machine learning in an abstract way, this course introduces them with a small number of examples. By using plotting and animations, insight into some of the mechanics of machine learning can be had. Furthermore, the student will gain practical skills in a case study, in which each step of developing a machine learning project is presented. By the end of this course, the student will have a solid understanding and experience with some of the fundamentals of machine learning enabling subsequent exploration.
Teacher: Weiguang Guan (SHARCNET, McMaster University)
Level: Introductory
Format: Lecture
Certificate: Completion
Prerequisites:
- Basic Python
Description: During this workshop, we will learn about ParaView, a free and open-source visualization tool for creating 3D visualizations of your datasets. In this interactive workshop you will get familiar with how ParaView works and at the end you should be able to generate basic visualizations of the demo data.
Teachers: Jarno van der Kolk (University of Ottawa)
Level: Beginner
Format: Interactive workshop
Certificate: Completion
Prerequisites: ParaView installed on your computer.
Description: This 90-minute workshop will provide attendees with an overview of text and data mining (TDM) at the University of Toronto Libraries. We’ll cover licensing–including emergent AI licensing clauses and standard license restrictions; APIs and access; content types and availability; and platforms that support TDM, especially ProQuest’s TDM Studio. You’ll leave knowing what is available at academic institutions, how to begin building a corpus, and basic modes of textual analysis.
Teachers: Neil Aitken (University of Toronto), Leslie Barnes (University of Toronto), Leanne Trimble (University of Toronto)
Level: Beginner
Format: Lecture / Demo
Certificate: Attendance
Prerequisites: None
Description: Apptainer is a secure container technology designed to be used on for high performance compute clusters. This workshop will focus on how to use Apptainer as well as how to make use of tools such as Conda and Spack within Apptainer. By the end of these sessions, one will have learnt about Apptainer and how it is installed and used on our computer clusters, how to build an Apptainer container image, how to install tools such as Conda/Spack from inside an Apptainer container shell, and,
how to use Apptainer containers within job submission scripts.
Teacher: Paul Preney (SHARCNET, University of Windsor)
Level: Introductory
Format: Lecture + Hands-on
Certificate: Completion
Prerequisite: Basic knowledge of Linux shell and how to run programs from the shell.