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 The repository contains material and exercises for the "Introduction to Machine Larning" material developed for [CODAS-HEP](https://codas-hep.org/) 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, PCA, autoencoders.
+- 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.