Minicourse on Machine Learning for Many-Body Physics

September 25 – 29, 2017

São Paulo, Brazil


logo.png (952×87)



Lecturers: Juan Felipe Carrasquilla (D-Wave Systems Inc., Canada) & Roger Melko (University of Waterloo & Perimeter Institute , Canada)

Place: IFT-UNESP Auditorium

Times: to be announced


This course will introduce modern machine learning techniques for studying classical and quantum many-body problems encountered in condensed matter, quantum information, and related fields of physics. Lectures will emphasize relations between statistical physics and machine learning, while tutorials will include hands-on experience in programming with applications.

Topics to be covered include lattice models for statistical physics, Monte Carlo methods, supervised and unsupervised learning, neural networks, Boltzmann machines, and deep learning. It would be useful if participants had basic knowledge of programming in any language. Tutorials will be given in Python and TensorFlow.

There is no registration fee and limited funds are available for travel and local expenses.

From October 23-28,2017 ICTP-SAIFR is organizing the School & Workshop on Density Functional Theory and Quantum Information Theory.  The online application is and more information can be found on the website.


Day 1: Statistical mechanics, Monte Carlo
- Lecture 1: Ising model, Gauge theories
- Lecture 2: Monte Carlo simulations
- LAB: Monte Carlo in Python

Day 2: General introduction to Machine Learning
- Lecture 1: Linear Fitting, Regression, Supervised learning
- Lecture 2: Supervised Learning for Ising systems and Backpropagation
- LAB: Feedforward Neural Network

Day 3: Supervised and Unsupervised Learning
- Lecture 1: Convolutional Neural Networks (CNNs)
- Lecture 2: Introduction to Unsupervised Learning, PCA
- LAB: CNN for Ising gauge theory

Day 4: Restricted Boltzmann Machines (RBMs)
- Lecture 1: RBMs for classical systems
- Lecture 2: RBMs for quantum systems
- LAB: An RBM for the Ising model

Day 5: Research Frontiers
- Lecture 1: Quantum State Tomography
- Lecture 2: Quantum Machine Learning
- LAB: Quantum tomograpy of the W state


  • Nathan Berkovits (ICTP-SAIFR & IFT-UNESP)
  • Alexandre Reily Rocha (IFT-UNESP)
  • Pedro Vieira (ICTP-SAIFR & IFT-UNESP & Perimeter Institute)



Poster_machine learning

Click here for online application

Registration deadline: August 25, 2017


Additional Information