This course is designed to introduce students to the field of physics-based animation by exposing them to the underlying mathematical and algorithmic techniques required to understand and develop efficient numerical simulations of physical phenomena such as rigid bodies, deformable bodies and fluids. In physics-based animation we will learn how to develop algorithms that produce visually compelling representations of physical systems. We will learn the underlying continuous mathematics describing the motion of physical objects, explore how to discretize them and how to solve the resulting discrete equations quickly and robustly. Topics covered include rigid body simulation, elasticity simulation, cloth simulation, collision detection and resolution and fluid simulation. Along the way, we will explore the underlying mathematics of ordinary differential equations, discrete time integration, finite element methods and more.

Computational and algorithmic systems are now embedded in all parts of society, which introduces both great opportunities and challenges. This course presents an introduction to computational social science, a rapidly growing interdisciplinary field that studies questions at the intersection of AI, data, and society. We focus on the spectrum of methodologies now available for conducting social research in the digital age, from large-scale observational studies to online experimentation, as well as research skills including reading state-of-the-art papers, writing reviews, and doing a research project. Topics covered will include online misinformation, algorithmic bias, and social media platforms.

This course explores the emerging mathematical foundations of algorithmic fairness across various decision-making contexts. Students will examine formal definitions of individual and (sub)group fairness, their relationships to each other and to the notion of bias, algorithms that provably satisfy them, and methods to audit existing algorithms for fairness. Application domains span social choice (resource allocation, voting, matching) and machine learning (classification, clustering). Students will apply these concepts through a project focused on examining fairness in a domain of their choice.

This course surveys algorithms that aid a group of biological or artificial agents in making collective decisions. The course specifically focuses on the area of computational social choice, which lies at the intersection of computer science and economics. This area has recently seen a growing number of real-world applications and this course reviews the theoretical foundations at the core of its success. The course will investigate issues which arise when a group of agents interact. This includes fairness, efficiency, preference elicitation, and strategic manipulations. These will be examined through frameworks of social choice theory (voting, fair division, matching, and facility location), mechanism design (auctions), and non-cooperative game theory (Nash equilibria, price of anarchy, and congestion games).

This course will explore how to use randomized A/B/N experiments to compare alternative intervention components and to continuously enhance the design of digitally delivered interventions, so that these interventions are intelligently adaptive in trying to better help people change behaviour and achieve their goals. The course draws on Human-Computer Interaction topics around design and prototyping of interventions, Social-Behavioural Science theory and methods for understanding and impacting human behaviour, Artificial Intelligence applications like LLMs (Large Language Models) for designing and adapting interventions, Machine Learning algorithms for adaptively analyzing and adjusting experiments, and Statistical methods for analyzing traditional and adaptive A/B/N experiments.

This course will cover qualitative and quantitative methods from human-computer interaction and how they are applied in research areas like healthcare and education. Examples of methods include participatory design, interviews, and field deployments. It will be offered in a seminar-style format involving readings of academic papers, student-led presentations, and a course project.

The deployment of machine learning in real world systems calls for a set of complementary technologies that will ensure that machine learning is trustworthy. Here, the notion of trust is used in its broad meaning: the course covers different topics in emerging research areas related to the broader study of security and privacy in machine learning. Students will learn about attacks against computer systems leveraging machine learning, as well as defense techniques to mitigate such attacks. The course assumes students already have a basic understanding of machine learning. Students will familiarize themselves with the emerging body of literature from different research communities investigating these questions. The class is designed to help students explore new research directions and applications.

This is a topics course in computer science. The topic will change from year to year and may span a variety of different topic in computer science.

This course will cover innovative human-computer interaction technologies that are being used across disciplines to solve real-world problems. These technologies include but are not limited to virtual reality (VR), augmented reality (AR), haptics, and generative AI. It will be offered in a seminar-style format involving readings of academic papers, student-led presentations, and a course project.

Students will learn fundamental methodologies, tools, and concepts for continuum robotics. The course covers continuum robot structures, constant curvatures kinematics frameworks, inverse and differential kinematics, trajectory planning and collision free motion planning, as well as sensing and control.

Words are fundamental components of human language, but their meanings tend to change over time; e.g., face ('body part' -> 'facial expression'), gay ('happy' -> 'homosexual'), mouse ('rodent' -> 'device'). Changes like these present challenges for computers to learn accurate representations of word meanings — a task that is crucial to natural language systems. This course explores data-driven computational approaches to word meaning representation and semantic change. Topics include latent models of word meaning (e.g., LSA, word2vec), corpus-based detection of semantic change, probabilistic diachronic models of word meaning, and cognitive mechanisms of word sense extension (e.g., chaining, metaphor). The course involves a strong hands-on component that focuses on large-scale text analyses and seminar-style presentations.

Computing and Global Development is designed to develop a critical understanding of international development and its relationship with computing technologies. This course will draw from various established and growing fields of academic scholarship including Information and Communication Technology and Development (ICTD), Human-Computer Interaction (HCI), Development Sociology, Science and Technology Studies (STS), and Political Economy.

This course introduces critical social analysis of the ethical aspects of Artificial Intelligence. Students will learn about the theories of ethics, the history of AI, the intersection between ethics and computing, the underlying values of AI, privacy concerns around AI applications, different kinds of biases (based on race, gender, age, sexual orientation, faith, geographic location, etc.) associated with many AI applications, the concerns around AI, and associated debates based on contemporary examples. This course will prepare students for systematically analyzing and auditing an AI system for its ethical standards, and designing new systems that are fairer.

This is a topics course in robotics. Topics will vary year to year.

This course will examine some of the most important papers in imitation learning for robot control, placing more emphasis on developments in the last 10 years. Its purpose is to familiarize students with the frontiers of this research area, to help them identify open problems, and to enable them to make a novel contribution.

An introduction to mobile robotic systems from a computational, as opposed to an electromechanical, perspective. Definitional problems in robotics and their solutions both in practice and by the research community. Topics include algorithms, probabilistic reasoning and modeling, optimization, inference mechanisms, and behaviour strategies.

This course will examine the growing prominence of mobile health over the past twenty years. After briefly discussing various definitions of mobile health, we will focus our attention on how people are using the sensors embedded in ubiquitous and novel devices to capture indicators of physical and mental health. More specifically, we will study how sensors can be used to measure physiological signals, psychomotor function, and disease-specific symptoms.

This is a special reading course in computer science. Topics change from year to year. Enrolment in this course requires departmental approval.

The MScAC degree is intended to create technical leaders capable of transferring new technologies from academia to industry. This course helps students to develop the skills required to be successful in finding an internship or future employment, as well as skills required to succeed in a business environment. The specific topics covered will vary from offering to offering, but will usually cover enhancing personal brand through resumes, cover letter,s and online profiles; job search action plans; interview training and strategy, and effective professional communication skills.

The MScAC degree is intended to create technical leaders capable of transferring new technologies from academia to industry. This course introduces fundamental business and management concepts relevant to students thinking about starting their own business or bringing new ideas to fruition within existing ones. This course will also equip students with the experience of presenting and defending their scientific research through various research activities and communications. The specific topics covered will vary from offering to offering, but will usually cover business and research innovation, research portfolio management, entrepreneurship, and market validation.

Through an eight-month industrial research internship, students will be required to demonstrate that they are able to translate some novel research idea into practice. Students will be expected to present the results of their internship to both the department and its industrial partners upon completion of their degree.

This course is unlike any other graduate course you have taken. You will play games, solve puzzles, and tell stories. Each activity will create a system around you, with its own dynamics. Sometimes you will try to beat the system and discover you cannot. Other times you will discover you can change a system by changing your perspective of it. In the process, you will discover how complex patterns of behaviour can arise from simple structures and simple rules. You will draw on such insights to develop a deeper understanding of how the world works. You will start to see the systems around you in a whole new light, and you will develop a new mental toolkit for analyzing complex global issues, modeling their structure and behaviour, and understanding how and why change happens. Along the way, you will read about the theory and practice of systems thinking, trace the history of the key ideas, and discover how they have been applied. You will explore how systems thinking provides new ways of studying the relationships between the most important global challenges of the twenty-first century, including globalization, climate change, conflict, democracy, energy, health and well-being, and food security.

Key topics will include: General Systems Theory, developed by Bertalanffy for understanding biological systems; Cybernetics: the study of feedback and control in living organisms, machines, and organizations; Systems Dynamics approaches for modelling and analyzing non-linear feedback mechanisms in complex systems; Complexity science and complex adaptive systems; The role of computational modelling and simulation as a central tool for understanding systems; Philosophical roots of systems thinking as a counterpoint to the reductionism used widely across the natural sciences; Emergent concepts from systems thinking, such as limits to growth, planetary boundaries, tipping points, sustainability, resilience, and chaos; Soft Systems Methodology and Critical System Theory for engaging multiple stakeholders in processes of change; Use of systems thinking to explore competing perspectives, trans-disciplinary synthesis, and modelling of global dynamics.

A major research project demonstrating the student's ability to do independent work in organizing existing concepts and in suggesting and developing new approaches to solving problems in a research area. The standard is that it could reasonably be submitted as a paper for peer-reviewed publication.

This is a required course for master's students (and doctoral students who did not take it in their masters programs). The aim of this course is to apply theory and research to the study of curriculum and teaching. The course (a) provides a language for conceptualizing educational questions; (b) reviews the major themes in the literature; c) provides a framework for thinking about curriculum changes and change; and (d) assists students in developing critical and analytical skills appropriate to the scholarly discussion of curriculum and teaching problems.

In this course we will identify ways that systems of oppression and oppressive educational practices manifest themselves in school settings - for example, within interactions between teachers and students; administrators and students; students and students; students and the curriculum; teachers and the curriculum; administrators and teachers; teachers and parents; parents and administrators - and we will discuss how we can use these spaces or locate new ones to do anti-oppressive educational work in school settings. Emphasis in the course will be placed on integrating anti-oppressive educational theory with anti-oppressive educational practice. We will attempt to link our discussions of practice to theory and our discussions of theory to practice.

This course provides for practical experience of as well as understanding of innovative practices in cooperative learning (CL). We explore rationales for and current developments (synergy, shared leadership). Topics include: What is CL (principles, attributes); how to organize CL (structures and strategies); how does CL work (basic elements, types of groups); teacher and student roles; benefits (positive interdependence, individual accountability, social skills, cohesion); evaluation (forms and criteria); obstacles and problems; starting and applying CL in your classroom (teachers' practical knowledge; collegiality; parental involvement); independent learning and collaborative inquiry; Ministry and Board requirements; and resources and materials Group (response trios) projects and joint seminars.

Experiential learning for students new to qualitative inquiry is provided through a broad introduction to qualitative approaches from beginning to end. A range of approaches relating to students' theoretical frameworks are explored. Thesis students are encouraged to pilot their thesis research.

This course will examine the foundations of educational thought from the perspectives of Jacques Derrida, Jean-Francois Lyotard, Luce Irigaray, Hélène Cixous, Michel Foucault, Roland Barthes, Gilles Deleuze, Julia Kristeva, Emmanuel Levinas, and Jean Baudrillard. Educational implications and applications of poststructural philosophy will be stressed in relation to the discursive and non-discursive limits of the scene of teaching.

A critical review of current approaches to analysing teaching and an examination of theoretical literature on the concept of teaching. The course involves reflection on one's own teaching. Students should be currently teaching or have access to a teaching situation. This course is most suitable for primary and secondary teachers.