2025 Nordita Winter School on "Physics of Machine Learning & Machine Learning for Physics"

Scientific Programme

Scientific program

Bruno Loureiro

• Introduction to Machine Learning
• Mean-Field Models 101: Curie-Weiss Model
• The Replica Method for Generalized Linear Estimation
• The Approximate Message Passing Algorithm
• Statistical-to-Computational Gaps

Arno Solin

• Fundamentals of Probability Theory
• Probabilistic Machine Learning in a Nutshell
• Introduction to Generative Models
• Concepts and Model Families in Advances in Generative Modeling
• Hands-On Examples in Python

Florian Marquardt

• Reinforcement Learning
• Artificial Scientific Discovery
• Representation Learning, Active Learning, and Interfacing with Labs
• Non-Neural-Network Approaches
• Neuromorphic Computing

Ricardo Vinuesa

• Introduction to Turbulence and Fluid Mechanics
• Temporal and Spatial Predictions with Deep Learning
• Explainable Deep Learning and Dimensionality Reduction
• Optimization and Flow Control

Riddhi Gupta

• Introduction to Quantum Systems
• Circuit-Based Model for Quantum Computing
• Example of a Quantum Machine Learning Algorithm
• Effect of Noise on Quantum Computation
• Execution of Quantum Machine Learning on Coding Platforms

Evert van Nieuwenburg

• Reinforcement Learning
• Optimization and Control of Quantum Devices
• Quantum Reinforcement Learning

Michael Spannowsky

• Introduction to ML Methods in High Energy Physics (HEP)
• Classification and Anomaly Detection of New Physics in HEP Data
• Use of Neural Networks to Perform Calculations in Field Theory
• Quantum Machine Learning Methods Applied to These Tasks

Anna Dawid

• Interpretable and Explainable Machine Learning
• Optimization and Loss Landscapes of Neural Networks
• Differences Between Optimizers
• Hands-On Tutorials