• Nikhef Topical Lectures on Effective Field Theories, lecturer on the topic of “Constraints on EFTs from high-energy colliders”. The full list of lectures is available from the Indico page, while my lectures are based on these lecture notes, condensed into these handout notes.


  • Applications of Machine Learning for Electron Microscopy, guest lecture at the Electron Microscopy Characterisation of the Nanoscale course, Master program in Applied Physics, Delft University of Technology, June 2021. Slides available here, and the Jupyter notebook with the hands-on tutorial can be found here.
  • Machine Learning for Physics and Astronomy, 3rd year course in the Natuur- en Sterrekunde BSc program at the UvA/VU (joint degree), Honours Track. The lecture slides and recordings, Jupyter notebooks for the tutorials and additional material can be found in the course GitHub repository, https://github.com/LHCfitNikhef/ML4PA. Part of the course is given by guest lecturers who present specific applications of machine learning to different areas of physics and astronomy.
    • Lecture 1: basic concepts, supervised learning, model fitting, regularisation, the bias/variance tradeoff. Video recordings of the lecture: Part A; Part B.
    • Lecture 2: optimisation strategies, stochastic gradient descent, deep neural networks, backpropagation. Video recordings of the lecture: Part A+B.
    • Lecture 3: supervised learning for classification, logistic regression, random forests, support vector machines. Video recording of the lecture: Part A+B.
    • Lecture 4: unsupervised learning, clustering, dimensional reduction, data visualisation, ensemble methods and bootstrapping. Guest lecture: machine learning in astronomy (A. Chhotray), slides. Video recordings of the lecture: Part A (unsupervised learning), Part B (guest lecture: machine learning in astronomy), Part C (ensemble methods), Part D (dimensionality reduction and data visualisation).
    • Lecture 5: convolutional neural networks, Bayesian neural networks, reinforcement learning. Video recording of the lecture: Part A (reinforcement learning), Part B (convolutional neural networks and bayesian NNs), Part C (ML in astroparticle physics). Guest lecture: Bayesian posterior estimation with classification networks (C. Weniger), slides and direct link.
    • Lecture 6: adversarial learning, generative adversarial networks. Video recording of the lecture: Part A (Information Theory revisited), Part B (generative models in ML). Slides and recording of Part C (adversarial network and generative adversarial learning). Slides and recording of Part D (Guest lecture by S. Caron on ML for HEP) available via Canvas.
    • Lecture 7: Part A: kernel methods and the dual representation, Gaussian processes. Part C: applications of machine learning for condensed matter (guest lecture by T. Bereau). Video recordings: Part A (Kernel methods and support vector machines), Part B (gaussian processes), Part C (machine learning in condensed matter).


  • 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).
    • Lecture notes corresponding to the first part of the course available here: from the principles of quantum theory to multi-electron atoms.
    • Complete set of lecture notes (updated 19/02/2020) available here.
    • Some video recorded lectures from 2017-2018 available here.
  • 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. Lecture notes available here (version 4/2/2020, work in progress). Some previous notes on SMEFT lectures from the Nikhef Topical Lectures in Flavour Physics available also here.
  • 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.
    • Tutorial 1: Supervised Learning for regression: training deep neural networks with TensorFlow (notebook)
    • Tutorial 2: Supervised Learning for classification: logistic regression for signal/background separation at the LHC (notebook)
    • Tutorial 3: Unsupervised Learning: clustering algorithms (notebook)
  • 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.


  • 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: