Machine Learning (2021/2022)

14 talks

April 04, 2022 - May 06, 2022

C22023

Collection TypeCourse

Source RepositoryPIRSA

DescriptionThis 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.

Displaying 1 - 12 of 14

Machine Learning (2021/2022)

Roger Melko University of Waterloo

May 05, 2022

PIRSA:22050011
Machine Learning (2021/2022)

Juan Carrasquilla ETH Zurich

May 03, 2022

PIRSA:22050010
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

May 02, 2022

PIRSA:22050009
Machine Learning (2021/2022)

Mohamed Hibat Allah

April 28, 2022

PIRSA:22040075
Machine Learning (2021/2022)

Mohamed Hibat Allah

April 26, 2022

PIRSA:22040074
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 25, 2022

PIRSA:22040073
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 21, 2022

PIRSA:22040072
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 19, 2022

PIRSA:22040071
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 18, 2022

PIRSA:22040070
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 14, 2022

PIRSA:22040069
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 12, 2022

PIRSA:22040068
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 11, 2022

PIRSA:22040067

Machine Learning (2021/2022)

Roger Melko University of Waterloo

May 05, 2022

PIRSA:22050011

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.
Machine Learning (2021/2022)

Juan Carrasquilla ETH Zurich

May 03, 2022

PIRSA:22050010

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

May 02, 2022

PIRSA:22050009

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.
Machine Learning (2021/2022)

Mohamed Hibat Allah

April 28, 2022

PIRSA:22040075

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.
Machine Learning (2021/2022)

Mohamed Hibat Allah

April 26, 2022

PIRSA:22040074

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 25, 2022

PIRSA:22040073

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 21, 2022

PIRSA:22040072

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 19, 2022

PIRSA:22040071

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 18, 2022

PIRSA:22040070

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 14, 2022

PIRSA:22040069

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 12, 2022

PIRSA:22040068

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.
Machine Learning (2021/2022)

Lauren Hayward Perimeter Institute for Theoretical Physics

April 11, 2022

PIRSA:22040067

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.

Title	Speaker(s)	Date	Talk Type	Info
Machine Learning (2021/2022)	Roger Melko	2022-05-05	Course	View details
Machine Learning (2021/2022)	Juan Carrasquilla	2022-05-03	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-05-02	Course	View details
Machine Learning (2021/2022)	Mohamed Hibat Allah	2022-04-28	Course	View details
Machine Learning (2021/2022)	Mohamed Hibat Allah	2022-04-26	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-04-25	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-04-21	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-04-19	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-04-18	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-04-14	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-04-12	Course	View details
Machine Learning (2021/2022)	Lauren Hayward	2022-04-11	Course	View details

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Machine Learning (2021/2022)

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