Material for a UC Irvine course offered by the Department of Physics and Astronomy.

Content is maintained on github and distributed under a BSD3 license.

- Setup your environment
- Reading list
- Homework assignments
**Course introduction slides**(older notebook)- Notebooks and numerical python
- Handle data
- Visualize data
**Tensor computing****Find structure in data****Measure and reduce dimensionality**- Adapt linear methods to nonlinear problems
**Estimate probability density****Probability theory****Statistical methods****Bayesian statistics****Markov-chain Monte Carlo in practice****Stochastic processes and Markov-chain theory****Variational inference****Optimization**- Computational graphs and probabilistic programming
**Bayesian model selection**- Learning in a probabilistic context
**Case Study: Redshift Inference**- Supervised learning in Scikit Learn
- Cross validation
**Neural networks: introduction****Neural networks: best practices****Supervised deep learning****Unsupervised deep learning**- Deep learning examples in tensorflow

**Boldfaced** entries are the suggested primary topics for a ten-week course.