New Graduate Course Available 鈥� Advanced Seminar in Learning Sciences: Machine Learning - Theory and Applications
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:
- 听Graduate students across the campus.
- 听Does not require any prior programming experience.
- 听Students who will learn to implement ML algorithms in Python or R.
- 听Gain a deeper understanding of the finer details of these algorithms.
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Dr. Maria Cutumisu
Associate Professor (Learning Sciences), 平特五不中鈥檚 Faculty of Education,
Department of Educational and Counselling Psychology
Dr. Cutumisu鈥檚 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 鈥� April 12, 2024; Thursdays 8:35 AM 鈥� 11:25 AM
Location:听 Education Building Room 437
Registration CRNs:听听 EDPE 668 001 Advanced Seminar in Learning Sciences: Machine Learning 鈥� Theory and Applications (CRN 2443)
For more info contact ecpinfo.education [at] mcgill.ca ()
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