-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
-
Machine Learning (2021/2022)
Mohamed Hibat Allah - University of Waterloo
-
Machine Learning (2021/2022)
Mohamed Hibat Allah - University of Waterloo
-
Machine Learning (2021/2022)
Lauren Hayward - Perimeter Institute for Theoretical Physics
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.