This course is designed to introduce machine learning techniques for studying classical and quantum many-body problems encountered in quantum matter, quantum information, and related fields of physics. Lectures will emphasize relationships between statistical physics and machine learning. Tutorials and homework assignments will focus on developing programming skills for machine learning using Python.
Format results
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Machine Learning Lecture - 230328
Mohamed Hibat Allah Perimeter Institute for Theoretical Physics
PIRSA:23030036 -
Machine Learning Lecture - 230327
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Joan Arrow University of Waterloo
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Sarah Marsh City of Kitchener
PIRSA:23030041 -
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Machine Learning Lecture - 230323
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030035 -
Machine Learning Lecture - 230321
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030034 -
Machine Learning Lecture - 230320
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030040 -
Machine Learning Lecture - 230314
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030032 -
Machine Learning Lecture - 230309
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030031 -
Machine Learning Lecture - 230307
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030030 -
Machine Learning Lecture - 230306
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030038 -
Machine Learning Lecture - 230302
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030029 -
Machine Learning Lecture - 230228 pt 2
Lauren Hayward Perimeter Institute for Theoretical Physics
PIRSA:23030033