Machine Learning for Physics and Astronomy (Honours)

3 EC

Semester 1, period 1

5093MLFP3Y

Owner Bachelor Natuur- en Sterrenkunde (joint degree)
Coordinator dr. Emilia Olsson
Part of Bachelor Physics and Astronomy (Joint Degree), year 3

Course manual 2025/2026

Course content

In recent years, Machine Learning tools have demonstrated their impressive potential to tackle a range of challenging problems related to classification, optimization, discrimination, inter- and extrapolation, efficient parameterizations, and pattern generalization, among several others. In this course, we will explore an overview of the foundational concepts in Machine Learning and highlight some of their applications to various areas of Physics and Astronomy. The course will be a dynamical combination of theoretical concepts and formalism, discussion of practical applications, and hands-on tutorials by means of Python Jupyter notebooks.

Study materials

Literature

  • See course guide and Canvas page

Syllabus

  • See course guide Canvas page

Objectives

  • Students become familiar with, and understand the differences between, various classes of machine learning algorithms
  • Students are able to apply a selection of machine learning tools to data.

Teaching methods

  • Lecture
  • Presentation/symposium
  • Laptop seminar

Learning activities

Activity

Hours

Hoorcollege

14

Tentamen

3

Werkcollege

12

Self study

55

Total

84

(3 EC x 28 uur)

Attendance

Programme's requirements concerning attendance (TER-B):

  • Each student is expected to participate actively in each component of the programme that he/she signed up for. A student that does not attend the first two seminars of a course, will be administratively removed from the seminar group. A request for reregistration for the seminars can be applied to the programme coordinator.
  • If a student cannot attend an obligatory component of a programme's component due to circumstances beyond his control, he must report in writing to the relevant teacher as soon as possible. The teacher, if necessary after consulting the study adviser, may decide to issue the student a replacing assignment.
  • It is not allowed to miss obligatory commponents of the programme if there is no case of circumstances beyond one's control.
  • In case of participating qualitatively or quantitatively insufficiently, the examiner can expel a student from further participation in the programme's component or a part of that component. Conditions for sufficient participation are set down in advance in the course manual.

Additional requirements for this course:

n/a

Assessment

Item and weight Details

Final grade

1) homework assignments amount to 40% of the final grade; the rubric for the assignments is: 

  • nothing handed in: 0 points 
  • solution partially complete/correct: 1 point 
  • solution mostly complete/correct: 2 points 
  • fully complete and correct solution: 3 points 

Assignment should be completed individually, and submitted on the course’s canvas webpage. 

2) Literature review project presentation amounts to 60% of the final grade.  
Instructions: choose a ML4phys paper, review & explain it in a pedagogical manner in front of fellow students (in pairs) (20 minutes presentation, 10 minutes Q&A). Make sure to highlight: 

  • the physics problem that is being addressed 
  • the details of the machine learning algorithm adopted (and the motivation thereof) \
  • the connection of the paper with the course’s material/spirit/topics 
  • the main results, and the role played by the ML algorithm 
  • and how this approach improves over more traditional strategies 

Inspection of assessed work

via Canvas

Assignments

1) homework assignments amount to 40% of the final grade; the rubric for the assignments is: 

  • nothing handed in: 0 points 
  • solution partially complete/correct: 1 point 
  • solution mostly complete/correct: 2 points 
  • fully complete and correct solution: 3 points 

Assignment should be completed individually, and submitted on the course’s canvas webpage. 

2) Literature review project presentation amounts to 60% of the final grade.  
Instructions: choose a ML4phys paper, review & explain it in a pedagogical manner in front of fellow students (in pairs) (20 minutes presentation, 10 minutes Q&A). Make sure to highlight: 

  • the physics problem that is being addressed 
  • the details of the machine learning algorithm adopted (and the motivation thereof) \
  • the connection of the paper with the course’s material/spirit/topics 
  • the main results, and the role played by the ML algorithm 
  • and how this approach improves over more traditional strategies 

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer lecture exercise
1

Lecture 1: Introduction, overview/review of ML applications to physics

Lecture 2: Neural Networks 101
2

Lecture 3: Training NNs: introduction, overfitting, bias-variance tradeoff

Exercise session 1.1 Neural Networks
3

Exercise session 1.2 Neural Networks

Exercise session 1.3 Neural Networks
4

Lecture 4: Dimensionality reduction: Autoencoders, principle-component analysis (PCA), t-stochastic neighbor embedding, Physics Informed Machine Learning

Exercise session 2.1 Dimensionality reduction & Physics informed neural networks
5

Guest lecture Christoph Weniger 

Lecture 5: Classification: clustering, support vector machines, kernel methods

Exercise session 2.2 Dimensionality reduction & Physics informed neural networks
6

Guest lecture Maximillian Lip

Lecture 6: Generative models: Restricted Boltzmann Machines, Normalizing Flows, Diffusion models, GANs

Exercise session 3.1 Neural Point Estimation & Neural Posterior Estimation
7

Lecture 7: Machine learning in the literature and presentation preparations

Exercise session 3.2 Neural Point Estimation & Neural Posterior Estimation
8 Final student presentations Monday 20/10  

Contact information

Coordinator

  • dr. Emilia Olsson