I hold a Canadian Institute for Advanced Research (CIFAR) Artificial Intelligence Chair, am a fellow of the “Learning in Machines and Brains” CIFAR Program, and a fellow of the Alberta Machine Intelligence Institute (amii).
My interests are Computational Linguistics, Machine Learning and Neuroscience. My work combines all three of these areas to study the way the human brain processes language.
Models of language meaning (semantics) are typically built using large bodies of text (corpora) collected from the Internet. These corpora often contain billions of words, and thus cover the majority of the ways words are used. However, to build computer programs that truly understand language, and can understand more rare and nuanced word usage, we need algorithms that can generalize beyond common word usage. By collecting brain images of people reading, we can explore how the human brain handles the complexities of language, which could inspire the next generation of semantic models.
Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course covers a variety of learning scenarios (supervised, unsupervised and partially supervised), as well as foundational methods for regression, classification, dimensionality reduction and modeling. Techniques such as kernels, optimization and probabilistic graphical models will typically be introduced. It will also provide the formal foundations for understanding when learning is possible and practical. Prerequisites: one of CMPUT 340 or 418; one of STAT 141, 151, 235 or 265 or SCI 151; or consent of the instructor.Winter Term 2021
Prerequisite: consent of Department. [Faculty of Science]Fall Term 2020