Advanced Machine Learning for Physics (PhD 2025)

PhD Student's Registration to the course: link

Course Information and Syllabus 


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

Program:

General objective of the course is to familiarise with advanced deep learning techniques based on differentiable neural network models with different learning paradigms; to acquire skills in modelling complex problems, through deep learning techniques, and understand how to apply these techniques in different contexts in the fields of physics, basic and applied scientific research.

Topics covered include: recalls of differentiable artificial neural networks and use of the pytorch library for ANN design, learning paradigmas, ANN for visions: segmentation and object detections, generativeAI: autoregressive models, invertible models, diffusion models, uncertainty quantification on ANNs, Graph Neural Networks, Attention and Transformers, Reinforcement Learning, Energy Models, AI explainability, Quantum Machine Learning on near-term quantum devices.

Approximately 50% of the lectures are frontal lessons supplemented by slide projections, 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 to reach the required CFUs:  20h, 40h, 60h (60h corresponds to the entiere course).


Forum group (telegram group):

Calendar: 

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: