The material in this repo is part of a seminar at Princeton University. Feel free to use it as you like.
The seminar starts out with several lecture-style meetings on the fundamentals of machine learning and data science. It'll be mixed up with code demonstrations and paper discussions from recent astrostatistics literature.
- 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
- Flux estimation and its priors (Jim Bosch)
- A (non-traditional) introduction to TensorFlow (Dan Foreman-Mackey)
- Likelihood-free inference (Justin Alsing)