Course manual 2023/2024

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 present an overview of the foundational concepts in Machine Learning and highlight some of their applications to various areas of Physics and Astronomy, from particle and astroparticle physics to condensed matter. 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

Syllabus

See course guide

Practical training material

https://uva-hva.gitlab.host/r.vanmastrigt/ml4phys

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.
In addition to the above mentioned rules, in the first semester of the first year a student should be present in at least 80% of the seminars. Moreover, participation to midterm tests and obligatory homework is required. If the student does not comply with these obligations, the student is expelled from the resit of this course. Students in the double Bachelor's degree programme Mathematics and Physics are exempted from this requirement. In case of personal circumstances, as described in OER-A Article A-6.4, a different study plan will be made in consultation with the study advisor.

Additional requirements for this course:

n/a

Assessment

Item and weight	Details
Final grade
0.6 (60%) literature review project presentation
0.4 (40%) homework 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

Inspection of assessed work

via Canvas

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	-Introduction -overview/review of ML applications to physics -unsupervised ML: clustering, PCA	Dimensional reduction & clustering
2	-neural networks (NNs) -feed-forward & backpropagation -optimization -convolutional NN	gradient descent
3	unsupervised ML cont’d: -autoencoders -Generative Adversarial Networks -Restricted Boltzmann Machines	RBM tutorial
4	supervised ML: introduction -overfitting -bias-variance tradeoff	tbd
5	Guest lectures: - Alberto Pérez de Alba Ortı́z - Ryan van Mastrigt	tbd
6	supervised ML cont’d: -support vector machines -kernel methods	Lecture: how are physicists contributing to understanding how ML algorithms work?
7	student presentations	student presentations

Owner	Bachelor Natuur- en Sterrenkunde (joint degree)
Coordinator	Edan Lerner
Part of	Bachelor Physics and Astronomy (Joint Degree), year 3

Machine Learning for Physics and Astronomy (Honours)