Machine learning course

The content of this course was originally developed for the master in quantum science and technology in Barcelona, where we taught the Machine learning for quantum and classical systems course. The content of the course has been progressively expanded, updated and adapted, as we have been developing new mateiral and using this course to teach in multiple masters, schools, conferences, and workshops.

Please, visit the Course and Documentation page to get started!

Course Description

This course gives an introduction to machine learning and deep learning: starting from linear linear models al the way up to state of the art generative models. The material covers the following topics:

• What is learning?
• Linear Models (linear regression, polynomial regression and logistic
  regression)
• A probabilistic view of machine learning
• Fundamentals of deep learning
• Convolutional Neural Networks
• Restricted Boltzmann Machines
• Generative Models

The course combines theory and practice in the form of jupyter notebooks with python. We make extensive use of specific librairies such as numpy, PyTorch and fastai.

Some of the content has been adapted from our book in machine learning for the quantum sciences (Dawid et al. 2022), as well as other material such as our PhD theses.

Original instructors and main contributors

Dawid, Anna, Julian Arnold, Borja Requena, Alexander Gresch, Marcin Podzie, Kaelan Donatella, Kim A. Nicoli, et al. 2022. Modern Applications of Machine Learning in Quantum Sciences. https://doi.org/10.48550/ARXIV.2204.04198.