Introduction to Machine Learning for the Sciences

• URL: http://arxiv.org/abs/2102.04883v1
• Date: Mon, 8 Feb 2021 16:25:46 GMT
• Title: Introduction to Machine Learning for the Sciences
• Authors: Titus Neupert, Mark H Fischer, Eliska Greplova, Kenny Choo, Michael Denner
• Abstract summary: The notes start with an exposition of machine learning methods without neural networks, such as principle component analysis, t-SNE, and linear regression. We continue with an introduction to both basic and advanced neural network structures such as conventional neural networks, (variational) autoencoders, generative adversarial networks, restricted Boltzmann machines, and recurrent neural networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This is an introductory machine learning course specifically developed with STEM students in mind. We discuss supervised, unsupervised, and reinforcement learning. The notes start with an exposition of machine learning methods without neural networks, such as principle component analysis, t-SNE, and linear regression. We continue with an introduction to both basic and advanced neural network structures such as conventional neural networks, (variational) autoencoders, generative adversarial networks, restricted Boltzmann machines, and recurrent neural networks. Questions of interpretability are discussed using the examples of dreaming and adversarial attacks.

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