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NBI Summer School on Machine Learning in Physics *dates and more info pending
Provider: Faculty of Science

Activity no.: 5912-20-11-31There are no available seats 
Enrollment deadline: 01/06/2023
PlaceNiels Bohr Institute
Date and time03.07.2023, at: 00:00 - 00:00
Regular seats
ECTS credits5.00
Contact personCharles Steinhardt    E-mail address: steinhardt@nbi.ku.dk
Enrolment Handling/Course OrganiserCharles Steinhardt    E-mail address: steinhardt@nbi.ku.dk
Course workload
Course workload categoryHours
Preparation / Self-Study90.00
Course40.00
Sum130.00
Content
For more details, and to apply, please go to this link: https://dawn.nbi.ku.dk/events/summer-school-on-machine-leaning-in-physics/

Aim and content
The aim of the school is to introduce the students to the liveliest topics in machine learning, in particular their application to datasets in physics. During the morning sessions, the focus will be on lectures by local and visiting faculty. In the afternoons, students will apply these lessons to specific datasets, designed to foster less formal interactions between faculty and students. The school will introduce specific techniques and algorithms, such as clustering (tSNE, GMM, spectral), neural networks (including CNNs), and tree-based (RF, XGboost), both for classification and for regression tasks. Special focus will be put on the datasets typically encountered in “big data” physics, which will be provided by the speakers from their own research, ranging from particle physics and molecular dynamics to large astronomical data-sets. As part of the school, two short sessions will be devoted to industry applications, led by (Copenhagen-based) speakers, who successfully transitioned from academic research into the private sector.

Lecturers
Local lecturers at Niels Bohr Institute, University of Copenhagen:

Charles Steinhardt, Associate Professor, DAWN (course organizer)
Adriano Agnello, Assistant Professor, Dark Cosmology Centre
Troels Petersen, Associate Professor, Experimental Particle Physics

Guest lecturers:

Viviana Acquaviva, Professor
CUNY NYC College of Technology and Center for Computational Astrophysics at the Flatiron Institute, USA

Keith Butler, Staff Scientist
Scientific Computing Division, Rutherford Appleton Laboratories, UK

Gregory Dobler, Professor of Public Policy, Physics & Astronomy, and Data Science
University of Delaware, USA

Remarks
*Due to the ongoing COVID-19 situation, this summer school will unfortunately be postponed. New dates and information re. course description/lecturers to be updated once confirmed.

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