Faculty of Science

Applying Machine Learning in Physics Research
Provider: Faculty of Science

Activity no.: 5934-25-11-13	There are 60 available seats
	Enrollment deadline: 01/05/2025

Place	Niels Bohr Institute
Date and time	June 2025
Regular seats	60
ECTS credits	2.50

Contact person	Stine Stenfatt West E-mail address: west@nbi.ku.dk

Enrolment Handling/Course Organiser	Troels Christian Petersen E-mail address: petersen@nbi.ku.dk
Exam requirements	Attendance along with a final ML-in-science challenge to be handed in (Kaggle style).

Aim and content The course will give the student an introduction to knowledge of Machine Learning (ML) and its application in various parts of data analysis in science, physics in particular. The focus will be on application through examples and use of computers. Formal requirements A master degree in a science subject, with at least a year’s experience of programming in Python. Learning outcome Knowledge: • The fundamental methodologies in ML. • Various Loss-Functions measures. • The most commonly used ML algorithms. • Examples of ML usage on various types of data. Skills: • Be able to apply ML algorithms to dataset • Be able to optimise the ML performance • Be capable of quantifying and comparing ML performances Competences: • Understanding of ML methods • Knowledge of data analysis with ML • Ability to analyse data using ML in science • Capability of handling data non-uniformities, along with unbalanced and categorical data. Target group Ph.D. students in physics (particle, astro, bio, quantum, etc.) and other Ph.D. students in/with closely related subjects/challenges. Teaching and learning methods Each day will have two lectures (9-10 and 13-14) followed by practical exercises (10-12 and 14-17). Two evenings, we will have a mixture of curriculum/social events, which entail class instructions followed by theoretical exercises (i.e. no computers) and then common dinner. The excursion will (potentially) by a veteran train to Kronborg, which we then visit. Course fee: 3000 Kr. (standard), waived for students from 4EU+ universities.

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