Books
===

###### tags: `SciML-Lecture`

> This is a list of recommended books to accompany the lecture

1. [*Probabilistic Machine Learning: An Introduction*, by Kevin P Murphy](https://github.com/probml/pml-book)
2. [*Pattern Recognition and Machine Learning*, by Christopher M Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
3. [*Gaussian Processes for Machine Learning*, by Carl E Rasmusses, and Christopher KI Williams](https://gaussianprocess.org/gpml/)
4. [*Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control* by Steven L Brunton, and J Nathan Kutz](http://databookuw.com)
- Available through the university library as an e-book.
6. [*An Introduction to Statistical Learning*, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani](https://www.statlearning.com)
7. [*Patterns, Predictions, and Actions: A Story about Machine Learning*, by Moritz Hardt and Benjamin Recht](https://mlstory.org/index.html)
8. [*Dive into Deep Learning*, by Aston Zhang, Zachary C Lipton, Mu Li, and Alexander J Smola](https://www.d2l.ai/index.html)
9. [*Understanding Computational Bayesian Statistics*, by William M Bolstad](https://cds.cern.ch/record/1437903/files/9780470046098_TOC.pdf)