Courses

This course introduces the fundamental concepts of statistical inference and statistical learning. Topics include random variable, sampling theory, experimental design and A/B testing, and hypothesis testing frameworks. Prerequisite: AIST-COIS 1000H. Not open to students with credit for MATH 1051H.

This course continues the development of statistical learning concepts to establish a strong foundation for future courses in machine learning. Topics include regression and multiple regression, connections between linear models and non-linear frameworks such as neural networks and optional topics. Prerequisite: AIST 2000H. Not open to students with credit for MATH 1052H.

Artificial intelligence (AI) is increasingly involved in digital communication, media, and public relations. This course explores the theoretical foundations and practical applications of AI in various communication domains. The challenges and ethical implications of the evolving role of AI in communication and media are also examined. Prerequisite: 7.0 university credits or permission of instructor.

Cross-listed: COMM-3004H

An examination of philosophical questions pertaining to artificial intelligence (AI) and the role AI plays in society. The first part treats the history, nature, and limits of AI. The second part turns to more applied questions related to ethics, and the social and political implications of AI. Prerequisite: 7.0 university credits or permission of the department chair.

Cross-listed: PHIL-3270H, COIS-3270H

Building on classical propositional and first-order logic, this course introduces students to a variety of non-classical logics including modal logic, intuitionistic logic, and many-valued logic, as well as fuzzy logic. Prerequisite 7.0 university credits including PHIL 2410H or COIS 2320H.

Cross-listed: PHIL-3410H, COIS-3410H

Fundamental artificial intelligence (AI) and machine learning concepts, algorithms, and techniques. The focus is on why and what can be done, along with available tools and applications in solving real-world problems, rather than the finer details of how it could be built. The course uses TensorFlow with provided Python code. Prerequisite: COIS 1020H.

Cross-listed: COIS-3550H

Advanced statistical methods, especially in computational statistics: linear and generalized linear models; classification methods; variable selection; resampling; decision trees; neural networks; Markov Chain Monte Carlo methods; Bayesian estimation; multilevel and hierarchical models; and experimental design. Additional topics as time allows. Prerequisite: MATH 2560H or a minimum 75% in each of MATH 1051H and 1052H, or permission of instructor.

Cross-listed: MATH-3561H

Introduces the Large Language Models (LLMs), their impact, and application on natural language processing (NLP). Machine learning approaches are discussed. Students will learn how to build computational models for NLP, how to custom and fine-tune LLMs and how to optimize their performance. Prerequisite: COIS 2020H and AIST-COIS 3550H.

Cross-listed: COIS-3570H

An examination of pressing ethical issues raised by the transformative impacts of ArtificialIntelligence (AI) on our society and across the globe. Topics may include harm mitigation,autonomous systems, lethal autonomous weapons, surveillance, security, and privacy,manipulation and behaviour, fairness and bias, the future of work, and artificial moral agents. Prerequisite: 8.0 university credits including AIST 1000H or PHIL 1100H, or permission of the department.

Cross-listed: PHIL-4270H

Data mining is the extraction of previously unknown and potentially useful patterns from large data sets. Core topics include the statistical foundations of data mining, sampling mechanisms, missing data, clustering, classification, and outlier detection. Case studies cover a wide variety of applications such as astronomy and marketing. Prerequisite: 1.0 COIS credit at the 3000 or 4000 level and one of COIS 2020H or COIS-ADMN 2620H.

Cross-listed: COIS-4400H

This course develops students' capabilities to drive business innovation and entrepreneurship in the AI era. Through evidence-based frameworks and hands-on projects, students build both practical applications and entrepreneurial skills essential for identifying, creating, and capturing emerging business opportunities. Prerequisite: 12.0 university credits of which 5.0 must be ADMN credits including ADMN 3420H or 3430H, or permission of instructor.

Cross-listed: ADMN-4430H

Artificial intelligence is the study of those techniques which create perceptions of "machine intelligence" and "intelligent agents." Topics may include but are not limited to expert systems, various evolutionary learning systems such as genetic algorithms, genetic programming, and neural networks. The impact and ethics of artificial intelligence are also examined. Prerequisite: 10.0 university credits including COIS 2020H.

Cross-listed: COIS-4550H

A capstone course providing students with the practical experience in the analysis and design (but not implementation) of a real-world information system. The idea is to customize the general information systems of COIS 4850H to AI related systems which has a wide range of selections with today's extensive applications of AI techniques. Prerequisite: COIS 2020H. Not open to students with credit for COIS 4000Y or 4850H.

Cross-listed: COIS-4851H