Seminar in Mathematics, Physics & Machine Learning

*In MPML Lecture Series we will have introductory minicourses on topics of interest in the area. *

A data science driven approach to physics and mathematics

In this self-contained lecture series, we will look at a computational and data science driven approach to problems in physics and mathematics.

We will focus on explicit constructions in specific case studies which have emerged over the past decades.

Finally, we discuss some recent developments in using neural networks and machine-learning using the test case of mathematical problems related to geometries that crop up in string theory, namely Calabi-Yau geometries.

This subject has been a fruitful cross-fertilization between mathematics, physics and computer science.

The mini-course is aimed at advanced Masters' students and beginning Ph.D. students in physics, mathematics and engineering who do not need any prior exposure to these topics. All technical details necessary for understanding any of the problems we consider will be introduced at a level accessible to a non-specialist.

**The lectures will involve some live coding demonstrations however. **A basic familiarity with Mathematica would be helpful.

*Move the mouse over the schedule to see start and end times and complete clipped titles.*

Mon, 30 Jan 2023

Tue, 31 Jan 2023

Wed, 1 Feb 2023

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