Advanced Machine Learning for Physics (PhD 2024)

Course Information and Syllabus 


Contacts: Stefano Giagu (stefano.giagu [at] uniroma1.it) and Andrea Ciardiello (andrea.ciardiello [at] gmail.com)

Program:

The general objective of the course is to become familiar with advanced deep learning techniques based on differentiable neural network models with different learning paradigms; acquire skills in modeling complex problems, through deep learning techniques, and be able to apply them in different contexts in the fields of physics, basic and applied scientific research.

Topics covered include: general overview of differentiable artificial neural networks and use of the pytorch library for ANN design, training and testing. Basic architectures: MLP, Convolutional neural network, neural network for sequence analysis (RNN, LSTM/GRU). Bayesian-NN. Attention, Self-Attention, Transformers and Visual Transformers, Models for object detection and semantic segmentation and applications. Graph Neural Networks and Geometrical Deep Learning. Generative models based on VAE, GAN, autoregressive models, invertible networks, diffusion models, normalising flow, and generative GNNs. Advanced learning techniques:  transfer learning, domain adaptation, adversarial learning, self-supervised and contrastive learning, model distillation.  Explainable and interpretable AI. Quantum Machine Learning on near-term quantum devices.

Approximately 50% of the lectures are frontal lessons supplemented by slide, aimed at providing advanced knowledge of Deep Learning techniques. The remaining 50% is based on hands-on computational practical experiences that provide some of the application skills necessary to autonomously develop and implement advanced Deep Learning models for solving various problems in physics and scientific research in general.

Indispensable prerequisites: basic concepts in machine learning, python language programming, standard python libraries (numpy, pandas, matplotlib, torch/pytorch )


Depending on the requirements of your specific PhD course each students can decided how may lectures/hands-on to attend tu fulfil the required hours:  20h, 40h, 60h (60h corresponds to the whole course).


Discussion group (telegram group):

Calendar: (in preparation)

Lectures2024

Student's E-mail and data for the CINECA HPC system accounts

enter the requested data in the google spreadsheet document available here (by the end of March 2024)

Bibliography/References and detailed topics treated during lectures, slides, notebooks, etc.


Given the highly dynamic nature of the topics covered in the course, there is no single reference text. During the course the sources will be indicated and provided from time to time in the form of scientific and technical articles and book chapters.

Some classic readings on Deep Learning based on differentiable neural networks: