Introduction to Machine Learning

This repository contains supplemantary material to an introductionary talk on machine learning given in the Code Coffee Seminar at the University Observatory of Munich.

This tutorial covers basic principles like data standardization, dimensionality reduction, clustering algorithms, and decision trees.
It furthermore discusses the basics of PyTorch and design of fully connected feedforward neural networks, convolutional neural networds, and autoencoders.

The notebooks are availabe on Binder:

1. Data Standardization & Dimensionality Reduction
2. Principal Component Analysis: An Example
3. Clustering Algorithms
4. Decision Trees
5. PyTorch
6. Multilayer Perceptron
7. Convolutional Neural Networks
8. Autoencoder