Course

Reinforcement learning: Markov decision processes, policy gradient methods

Anomalies in the Standard Model

Dimensional reduction using t-distributed stochastic neighbour embedding (t-SNE); Kullback-Liebler (KL) divergence; maximum likelihood estimation

The state-operator map and sphere correlation function

States and Operators

Introduction to unsupervised learning; dimensional reduction using principal component analysis

Standard Model Effective Field Theory and neutrino SM

Ghost Hilbert space 

Convolutional neural networks: local receptive fields, shared weights and biases, pooling

Spontaneous symmetry breaking continued, the puzzles of SM and the solutions

BRST quantization: a toy model, gauge fixing; BRST quantization for string theory, ghosts in String.