Machine Learning for the Sciences

The material in this repo is part of a seminar at Princeton University. Feel free to use it as you like.

Approach

The seminar started out with several lecture-style meetings on the fundamentals of machine learning and data science. From there, it covers code demonstrations, research projects, and paper discussions from recent literature.

The "Lecture"

• Introduction and Introductory Example
• Kernel density estimation
• (Gaussian) mixture models
• Mixture model applications
• Clustering
• Classification overview and Theory for linearly separable cases
• Neural networks 101 and PyTorch introduction

Tutorials / Research

• Flux estimation and its priors (Jim Bosch)
• A (non-traditional) introduction to TensorFlow (Dan Foreman-Mackey)
• Likelihood-free inference (Justin Alsing)
• Sparsity and Deep Learning for modern surveys (Francois Lanusse)
• Practical Hamiltonian Monte Carlo in Python for Astronomers (Dan Foreman-Mackey)
• Forward, causal modeling of galaxy photometry: Hierarchical causal models ↔ machine learning (Boris Leistedt)
• Using deep convolutional neural networks to predict galaxy metallicity from three-color images (John Wu)
• Accelerating Scientific Calculations with GPUs (Evan Schneider)

Papers / Blogs

• The Building Blocks of Interpretability by Olah et al. (2018)
• Galaxy detection and identification using deep learning and data augmentation by González et a. (2018)
• The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy (2015)
• Understanding LSTM Networks by Christopher Olah (2015)
• Robort Burns by Adrian Price-Whelan
• The fall of RNN / LSTM by Eugenio Culurciello (2018)
• Deep neural networks to enable real-time multimessenger astrophysics by George & Huerta (2018)
• Galaxy detection and identification using deep learning and data augmentation by González et al. (2018)
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention by Xu et al. (2015)
• From Dark Matter to Galaxies with Convolutional Networks by Zhang et al. (2019)
• Hamiltonian Neural Networks by Greydanus et al. (2019)
• Differentiable Convex Optimization Layers by Agrawal et al. (2019)

Conferences

• Interpretable Learning in Physical Sciences October 14 - 18, 2019
• Theory of Deep Learning: Where next? October 15 - 18, 2019
• Machine Learning and the Physical Sciences December 14, 2019