The repository contains material and exercises for the "Introduction to Machine Larning" material developed for CODAS-HEP 2023. It is split into four parts that include background, theory and hands on exercises (that are HEP focused when possible):
- Part 1: Introduction to Machine Learning: Motivations and basics of machine learning, linear regression, logistic regression, optimization and regularization.
- Part 2: Supervised Deep Learning: basics of deep learning, backpropagation, optimization i.e. Multi Layer Perceptrons (MLPs).
- Part 3: Convolutional Neural Networks and Autoencoders: working with images, Convolutional Neural Networks (CNNs), motivation for unsupervised learning, autoencoders.
- Part 4: Permutation Invarience: deep sets, background on graphs as data representation, theory of graph neural networks, transformers.