Deep Learning in Physics (Spring 2021)

• Linear regression in Scikit-learn, implementation in NumPy, introduction to PyTorch

Deep Learning in Physics (Spring 2020)

• Perceptron, Shallow Neural Networks, Scikit-learn
• Regression and Classification problems
• Bias-Variance trade-off
• PyTorch
• Analysis of MNIST data with NN
• Convolutional Neural Networks and MNIST fashion data, LetNet
• Phase transition in Ising model
• Regularization, Bootstrap, DropOut, Balanced Sampling, Mean Variance
• Batch normalization
• TensorFlow 2.0, Keras
• Generative Neural Networks
• ML in Condensed Matter Physics and Particle Physics.

Introduction to neural computations (Spring 2019)

• Hopfield networks
• Linear discriminants
• Single neuron, perceptron
• Least-square method
• Multi-layer perceptron, XOR problem, Cybenko theorem
• Backpropagation of errors
• Relative entropy
• Bayesian neural networks

Obliczenia numeryczne i symboliczne, z wykorzystaniem Wolfram Mathematica

Neutrinos for layman (in polish)

Clasical Electrodynamics: 2014, 2013, 2012

• Special Relativity I, problems
• Special Relativity II, problems

Coulomb potential and Radiative Corrections

• Born corrections to ep scattering lecture 1, lecture 2.
• Radiative Corrections - Veltman-Passarino Integrals