Course

Monte Carlo sampling; summary of hyperparameters in feedforward neural networks

CKM matrix and spontaneous symmetry breaking

The string spectrum; BRST quantization, cohomological field theory, a basic model

How to write down the Standard model Lagrangian

The two-dimensional free boson: Fock space, symmetries, stress energy tensor, Virasoro algebra

Alternatives to gradient descent, backpropagation, recognizing overfitting

Review of gauge theory and standard model particle content.

String perturbation theory: Nambu-Goto action, Polyakov action, gauge fixing

Supervised learning with feedforward neural networks: weights and biases, activation functions, cost functions, gradient descent

BSM: the ultimate puzzle and mathematical tools.

General considerations on String theory (String perturbation theory, D-branes, dualities,....)