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 - 230330
Lauren Hayward Perimeter Institute for Theoretical Physics
23030037 -
Machine Learning Lecture - 230328
Lauren Hayward Perimeter Institute for Theoretical Physics
23030036 -
Machine Learning Lecture - 230327
Lauren Hayward Perimeter Institute for Theoretical Physics
23030041 -
Machine Learning Lecture - 230323
Lauren Hayward Perimeter Institute for Theoretical Physics
23030035 -
Machine Learning Lecture - 230321
Lauren Hayward Perimeter Institute for Theoretical Physics
23030034 -
Machine Learning Lecture - 230320
Lauren Hayward Perimeter Institute for Theoretical Physics
23030040 -
Machine Learning Lecture - 230314
Lauren Hayward Perimeter Institute for Theoretical Physics
23030032 -
Machine Learning Lecture - 230309
Lauren Hayward Perimeter Institute for Theoretical Physics
23030031 -
Machine Learning Lecture - 230307
Lauren Hayward Perimeter Institute for Theoretical Physics
23030030 -
Machine Learning Lecture - 230306
Lauren Hayward Perimeter Institute for Theoretical Physics
23030038 -
Machine Learning Lecture - 230302
Lauren Hayward Perimeter Institute for Theoretical Physics
23030029 -
Machine Learning Lecture - 230228 pt 2
Lauren Hayward Perimeter Institute for Theoretical Physics
23030033