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.
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.
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.