Teaching

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).
  • 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.

2018-2019

  • 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:

2017-2018

2016-2017

2015-2016