**2019-2020**

**Quantum, Atomic, and Molecular Physics**, part of the Medical Natural Sciences (*Medische Natuurwetenschappen MNW*) Bachelor program at the VU Amsterdam. Study guide and syllabus of the course available here, additional course materials available via the VU Canvas page (only for registered students).

**The Standard Model as an Effective Field Theory,**PhD course at the DRSTP (Dutch Research School of Theoretical Physics) school in Dalfsen (the Netherlands), February 2020. Short course aimed to Dutch PhD students in the area of Theoretical High Energy Physics.

**Quantum Field Theory (extension)**, part of the Master course in Physics and Astronomy, Theoretical Physics track. Topics covered include regularisation of divergences in loop QFT calculations, renormalisation in scalar QFTs, effective field theories, symmetries and quantisation of the Abelian gauge field, and scattering processes involving photons. The Study Guide of the course can be found here, with additional course materials available via the UvA Canvas page (only for registered students).**Lecture notes**of the 2019-2020 course can be found here.

**Introduction to Machine Learning for Physics and Astronomy,**graduate course at the Universidad Complutense de Madrid in the framework of the IPARCOS workshop “Applications of Machine learning and deep learning to Physics and Astronomy”. Madrid, December 2019. Slides of the course available here.

**Machine Learning: a New Toolbox for Theoretical Physics**, part of the**Advanced Topics in Theoretical Physics**aimed to the PhD students of the Delta Institute for Theoretical Physics (Delta-ITP) from Amsterdam, Utrecht, and Leiden. The Study Guide for the course can be found here and the course GitHub repository containing the course materials (lecture notes, examples for the tutorial sessions) is available here.- Lecture 1: Basic concepts and terminology in Machine Learning, supervised learning, model fitting and polynomial regression, regularisation and cross-validation, optimisers in ML, gradient descent and its variants, genetic algorithms and its variants.
- Tutorial 1 (iPython notebooks): model fitting with polynomial regression, gradient descent methods
- Lecture 2: concepts of statistical and bayesian learning, deep neural networks, backpropagation, regularisation of neural networks, supervised learning for classification and logistic regression.
- Lecture 3: dimensional reduction and data visualisation, principal component analysis, unsupervised learning, clustering, ensemble methods, bootstrapping, random forest and decision trees, reinforcement learning with Q-learning
- Lecture 4: convolutional neural networks, energy based models and Boltzmann learning, generative models and adversarial learning, generative adversarial networks, machine learning for quantum computation.

**2018-2019**

**The Standard Model Effective Field Theory**, part of the Nikhef Topical Lectures on Flavour Physics and CP violation.

**Van Quantum Tot Molecuul**, 2nd year course in the*Medische Natuurwetenschappen*BSc program at the VU Amsterdam. Lecture notes available below:

**Quantum Field Theory Extension**, MSc program in Physics and Astrononomy (joint UvA/VU degree). Lecture notes available here:

**Introduction to Elementary Particle Physics**, 1st year course in the Applied Physics bachelor program at the Technical University of Delft.- Lecture notes available here
- Slides lecture 1: Particle Physics in the Higgs boson era
- Slides lecture 2: Basic building blocks of the Standard Model
- Slides lecture 3: Fermion, bosons, and neutrinos (I)
- Slides lecture 4: Fermion, bosons, and neutrinos (II)
- Slides lecture 5: The strong interaction and hadron structure
- Slides lecture 6: The weak interaction (I)
- Slides lecture 7: The weak interaction (II)
- Slides lecture 8: The Higgs boson and collider physics

**Introduction to Particle Physics**, 2nd year course in the*Natuur- en Sterrekunde*BSc program at the UvA/VU (joint degree)- Guest lecture on Feynman diagrams for particle physics

**2017-2018**

**Nikhef Topical Lectures**, specialised graduate courses for PhD students, part of the Nikhef Topical Lectures on Machine Learning and Artificial Intelligence, April 2018*Machine Learning applications in High-Energy Physics*: Theory Vision, part of the Nikhef Topical Lectures on Physics at Future Colliders*Physics at Future High-Energy Colliders*

**2016-2017**

**Nikhef Topical Lectures**, specialised graduate courses for PhD students*Symmetries (and their breaking) in the Standard Model*, part of the Topical Lectures on Symmetries

**2015-2016**

, part of the Master Course in Mathematical and Theoretical Physics, University of Oxford*The Standard Model and LHC Phenomenology*- Lecture notes of the course available here