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2021-06 NITheCS Mini-School: Machine Learning in High Energy Theory
2021-06 NITheCS Mini-School: Machine Learning in High Energy Theory

Title: Machine Learning as a Discovery Tool in hep-th
Presenter: Vishnu Jejjala, School of Physics, University of the Witwatersrand
Date: 8 June 2021, 2 pm
Abstract: We present examples of the application of machine learning to problems in string geometry, quantum field theory, and mathematics. Based on these examples, we discuss current and future research directions in string data.

Title: Mathematics of Deep Learning
Presenter: Anirbit Mukherjee, Department of Statistics, University of Pennsylvania
Date: 15 June 2021, 2 pm
Abstract: We discuss how we can understand deep learning more rigorously. We introduce a few very simple networks with commonly used activators like ReLU and sigmoid activation and discuss why learning is possible with those setups.

Title: Basics of Machine Learning (I and II)
Presenter: Pallab Basu, School of Physics, University of the Witwatersrand
Dates: 22 & 29 June 2021, 2 pm
Abstract: I introduce the basics of machine learning and its application in physics. We discuss how to construct a DNN and CNN and apply them in the Ising model near the phase transition. I also introduce how to tackle deep learning problems using pytorch and jupyter notebook.
Jun 22, 2021 02:00 PM
Jun 29, 2021 02:00 PM
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