General Motivation

1 Basics on Machine Learning

• Basic Structure of Artificial Neural Networks 

• Training a Network

• Supervised Learning

• Convolutional Neural Networks

• Unsupervised learning

• Recurrent Networks

• Transformers 

• Boltzmann Machines

• Support Vector Machines 

• Reinforcement learning

2 Phase Transition in Lattice Gauge Theories

• Supervised and Unsupervised phase transition recognition in Spin Models

• Examples of Applications in LGT

Phase transition recognition in SU(2)
Phase transition recognition in SU(3)
Phase transition recognition in Lattice QCD 

3 Renormalisation Group and Machine Learning

• Machine Learning and Renormalization Group

• Deep Learning and Renormalisation Group

• Inverse Renormalisation Group in Lattice Field Theories 

4 Machine Learning for Quantum Field Theories with sign problem

• Contour Deformations 

• Complex Normalizing Flow 

• Applications

5 Machine Learning and Spectral Functions in Lattice QCD

• Machine Learning and the Inverse Problem

• Machine Learning Hadron Spectral Functions In Lattice QCD

• Applications

6 Flow Based Sampling

• Normalizing Flows

• Stochastic Normalizing Flows

• Fourier Flows

• Equivariant Flows 

• Applications

7 Machine Learning and Effective Field Theories

• Symbolic Computation using Machine Learning 

• Bayesian estimates for high orders in perturbation theory 

• Generative models in Effective Field Theories 

• Differentiable programming in Effective Field Theories

8 Estimation of Lattice QCD observables using Machine Learning

• Boosted decision tree regression ML algorithm

• Single Point Neural Networks & Global Neural Networks