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Advanced Seminar in Learning Sciences:
Machine Learning - Theory and Applications

Machine learning (ML) is used to make predictions, provide explanations, and gain insights and knowledge from the decision structures inferred from the data. This course conveys principles underlying the current practice of ML by focusing on fundamental ML algorithms that provide the technical basis of data mining. It 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 the models are produced, students will be able to develop rigorous data models, interpret them correctly, and identify their strengths and limitations.

This course is open to:

• ÌýGraduate students across the campus.
• ÌýDoes not require any prior programming experience.
• ÌýStudents who will learn to implement ML algorithms in Python or R.
• ÌýGain a deeper understanding of the finer details of these algorithms.
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Dr. Maria Cutumisu

Associate Professor (Learning Sciences), ³ÉÈËVRÊÓƵ’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.

Course Schedule: January 4 – April 12, 2024; Thursdays 8:35 AM – 11:25 AM
Location:Ìý Education Building Room 437
Registration CRNs:ÌýÌý EDPE 668 001 Advanced Seminar in Learning Sciences: Machine Learning – Theory and Applications (CRN 2443)
For more info contact ecpinfo.education [at] mcgill.ca ()

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