Machine Learning (2021/2022)

APA

Melko, R. (2022). Machine Learning (2021/2022). Perimeter Institute for Theoretical Physics. https://pirsa.org/22050011

MLA

Melko, Roger. Machine Learning (2021/2022). Perimeter Institute for Theoretical Physics, May. 05, 2022, https://pirsa.org/22050011

BibTex

          @misc{ scivideos_PIRSA:22050011,
            doi = {},
            url = {https://pirsa.org/22050011},
            author = {Melko, Roger},
            keywords = {},
            language = {en},
            title = {Machine Learning (2021/2022)},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2022},
            month = {may},
            note = {PIRSA:22050011 see, \url{https://scivideos.org/index.php/pirsa/22050011}}
          }
          

Roger Melko University of Waterloo

Source Repository PIRSA
Talk Type Course

Abstract

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.