This course gives an overview of different learning techniques and algorithms used in Artificial Intelligence (AI). The components involved in building, training, and utilizing AI models will be covered as well as the impact of bias, ethical, and societal issues.
Winter 2026 - Online Summer 2026 - Online
In this course, students will survey the growing field of Generative Artificial Intelligence, exploring techniques for generating data such as images, text and video using models like generative adversarial networks (GAN), variational auto-encoders, and large language models. The underlying frameworks and algorithms of building cutting-edge generative models will be explored. Offered winter semester. Dual-listed with AI 502.
Winter 2026 - Online
This course explores the ethical challenges and biases in Artificial Intelligence (AI) systems, focusing on data, algorithmic, and decision biases. Students will examine fairness metrics, bias mitigation strategies, generative Artificial Intelligence biases, and societal impacts, utilizing interdisciplinary approaches to create responsible, equitable artificial intelligence systems. Dual-listed with AI 511. Offered fall and winter semesters. Prerequisite: AI 201.
Summer 2026 - Online
This course gives an overview of different learning techniques, and algorithms used in Artificial Intelligence (AI). It helps students understand the components involved in building, training, and utilizing AI models. Students will learn about biases, ethical, and societal issues that arise from using AI models for making real-world decisions. Offered fall and winter semesters. Prerequisite: Admission to any master's program.
In this course, students will survey the growing field of Generative Artificial Intelligence, exploring techniques for generating data such as images, text and video using models like generative adversarial networks (GAN), variational auto-encoders, and large language models. The underlying frameworks and algorithms of building cutting-edge generative models will be explored. Offered winter semester. Dual-listed with AI 402.
This course explores the ethical challenges and biases in Artificial Intelligence (AI) systems, focusing on data, algorithmic, and decision biases. Students will examine fairness metrics, bias mitigation strategies, generative Artificial Intelligence biases, and societal impacts, utilizing interdisciplinary approaches to create responsible, equitable artificial intelligence systems. Dual-listed with AI 411. Offered fall and winter semesters. Prerequisite: AI 501.
