The listed courses are newly offered by the Faculty of Science in 2026.
Review how to find course details in Workday and check out course descriptions, including prerequisites, in the Academic Calendar. You can also refer to resources for course registration.
AI_V 100: Introduction to Artificial Intelligence (3 credits)
Core methodological paradigms in AI. Strengths and limitations of modern AI systems. Social impacts of AI technologies: economic, cultural, scientific, environmental, political. Philosophical implications and plausible future trajectories for AI.
AI_V 240: Introduction to Machine Learning (4 credits)
Core methodological paradigms in machine learning. Models for supervised prediction and unsupervised analysis. Deep learning techniques using differentiable programming.
AI_V 322: Foundations of Artificial Intelligence (3 credits)
Introduction to representation and reasoning via topics such as Search, problem-solving and planning, logic, probabilistic graphical models, preference models, Markov decision processes and reinforcement learning, as well as multi-agent decision making.
AI_V 360: Deep Learning (3 credits)
Advanced machine learning focused on deep learning in practice: mathematical foundations, models for unstructured, sequential, spatial, and graph data, methods for prediction, generative modeling, and reinforcement learning, and hands-on experience designing, optimizing, tuning, and applying deep models.
CPSC_V 337: Introduction to Computer Security (3 credits)
Fundamentals of computer security and privacy. Cryptography, network security, and application security. Security assessments, problem diagnosis, and defensive strategies. Impacts and outcomes of security design decisions.
CPSC_V 423: Natural Language Processing (3 credits)
Fundamentals of data-driven natural language processing, including applications (machine translation, summarization, question answering), text representations (word embeddings, language models), and statistical and neural network methods.
EOSC_V 417: Stable Isotopes in Earth, Ocean and Environmental Sciences (3 credits)
Principles of stable isotope geochemistry applied to environmental systems, including instrumentation, data and sample analysis, and applications to (paleo) climate, oceanographic and environmental research.
PATH_V 200: Pathology and Laboratory Medicine - Myths and Misconceptions (3 credits)
This is a course from the Faculty of Medicine introducing pathology and laboratory medicine concepts in simple terms and seeking to debunk myths and misconceptions surrounding these disciplines. It provides students with a holistic understanding of the roles played by pathology and laboratory medicine in understanding, preventing, and treating disease.
SCIE_V 466: New Venture Design (6 credits)
Teams comprising students in SCIE_V 466 and COMM_V 466 create a business plan and a prototype or scientific solution of a novel product, process, or process component.
STAT_V 303: Introduction to Probabilistic Modelling and Simulation (3 credits)
Core concepts in probability taught through probabilistic generative modelling, simulation of random variables, basic Monte Carlo, common probability distributions and models, basic probabilistic model design and inference.
STAT_V 416: Advanced Linear Modelling (3 credits)
Advanced topics in linear modelling, including weighted least squares, model selection, robust methods, splines, Bayesian methods, mixed effects models, and generalized linear models.
