Machine Learning (2021/2022)


Hayward, L. (2022). Machine Learning (2021/2022). Perimeter Institute for Theoretical Physics. http://pirsa.org/22050010


Hayward, Lauren. Machine Learning (2021/2022). Perimeter Institute for Theoretical Physics, May. 03, 2022, http://pirsa.org/22050010


          @misc{ scitalks_22050010,
            doi = {},
            url = {http://pirsa.org/22050010},
            author = {Hayward, Lauren},
            keywords = {},
            language = {en},
            title = {Machine Learning (2021/2022)},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2022},
            month = {may},
            note = {Talk #22050010 see, \url{https://scitalks.ca}}

Lauren Hayward Perimeter Institute for Theoretical Physics

Source Repository PIRSA
Talk Type Course


This course is designed to introduce modern machine learning techniques for studying classical and quantum many-body problems encountered in condensed matter, quantum information, and related fields of physics. Lectures will focus on introducing machine learning algorithms and discussing how they can be applied to solve problem in statistical physics. Tutorials and homework assignments will concentrate on developing programming skills to study the problems presented in lecture.