Introduction to machine learning and its application to physics: Difference between revisions
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Machine learning (ML) is now permeating into almost every field of science; physics seems to be no exception. Recently, many overseas groups are utilizing ML to attack hard-to-solve problems (mostly) in the context of many-body physics. Although concrete insight is still far from our grasp via this technique, ML often shows good performance beyond our expectation. Following the trend of using ML as a data-analyzing or feature-extracting tool for physics, this talk will briefly touch on the history, types, and background theories of ML; it will also browse through some recent studies in this line. | Machine learning (ML) is now permeating into almost every field of science; physics seems to be no exception. Recently, many overseas groups are utilizing ML to attack hard-to-solve problems (mostly) in the context of many-body physics. Although concrete insight is still far from our grasp via this technique, ML often shows good performance beyond our expectation. Following the trend of using ML as a data-analyzing or feature-extracting tool for physics, this talk will briefly touch on the history, types, and background theories of ML; it will also browse through some recent studies in this line. | ||
{{Media/Button|L-CW2018d.pdf|Talk slides}} | |||
[[Category:Seminars]] | [[Category:Seminars]] | ||
[[Category:Condensed Matter Seminars]] | [[Category:Condensed Matter Seminars]] | ||
Latest revision as of 01:15, 30 October 2018
- Speaker: Dr. Lee, Chang-Woo (KIAS)
- Date: Wednesday February 21, 2018 05:00pm
- Place: Jungho Seminar
Machine learning (ML) is now permeating into almost every field of science; physics seems to be no exception. Recently, many overseas groups are utilizing ML to attack hard-to-solve problems (mostly) in the context of many-body physics. Although concrete insight is still far from our grasp via this technique, ML often shows good performance beyond our expectation. Following the trend of using ML as a data-analyzing or feature-extracting tool for physics, this talk will briefly touch on the history, types, and background theories of ML; it will also browse through some recent studies in this line.