Machine Learning (2021/2022)
APA
Carrasquilla Álvarez, J.F. (2022). Machine Learning (2021/2022). Perimeter Institute. https://pirsa.org/22050010
MLA
Carrasquilla Álvarez, Juan Felipe. Machine Learning (2021/2022). Perimeter Institute, May. 03, 2022, https://pirsa.org/22050010
BibTex
@misc{ pirsa_PIRSA:22050010, doi = {}, url = {https://pirsa.org/22050010}, author = {Carrasquilla {\'A}lvarez, Juan Felipe}, keywords = {}, language = {en}, title = {Machine Learning (2021/2022)}, publisher = {Perimeter Institute}, year = {2022}, month = {may}, note = {PIRSA:22050010 see, \url{https://pirsa.org}} }
ETH Zurich
Talk number
PIRSA:22050010
Collection
Talk Type
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.