ÎÛÎÛ²ÝÝ®ÊÓƵ

An exploration of the principles underlying the current practice of machine learning (ML) by focusing on fundamental ML algorithms applied to many domains. This course is designed to help students learn to think critically about data and models, understand the conceptual underpinnings of the basic ML algorithms and techniques, how they work, how to choose an algorithm for each kind of learning task, and how to visualize, evaluate, and interpret performance measures and results correctly.

By understanding how models are produced, students will be able to develop rigorous data models, interpret them correctly, and identify their strengths and limitations.
Ìý

Dr. Maria Cutumisu

Associate Professor (Learning Sciences), ÎÛÎÛ²ÝÝ®ÊÓƵ’s Faculty of Education,
Department of Educational and Counselling Psychology

Dr. Cutumisu’s research draws on computing science and educational psychology and has been funded by tri-council grants and scholarships. She graduated with an M.Sc. and a Ph.D. in Computing Science from the Department of Computing Science, University of Alberta and she trained as a postdoctoral scholar in Learning Sciences at the Stanford Graduate School of Education.

Her research interests include feedback processing and memory, machine learning and educational data mining for automated feedback generation, AI in games, computational thinking, and data literacy.
Ìý

Course Schedule:
January 6 – April 11, 2025; Fridays 8:35 AM – 11:25 AM

Location:
Leacock Building – Room 110

Registration CRNs:
EDPE 561 - Artificial Intelligence in Education
(CRN 7129-001/7130-002)
Ìý