# Advanced Scientific Machine Learning
This book is a collection of notes on advanced scientific machine learning. I develop it as part of the course ME 697 (with a similar name) at Purdue University. The book is intended for graduates students in engineering and science who want to learn about machine learning in the context of scientific applications.
## Prerequisites
This book is not an introduction to machine learning.
You cannot start this book without a good basic understanding of the topic.
Also, you can probably not follow the book unless you are familiar with the basics of scientific computing and numerical methods.
You need a fairly strong understanding of linear algebra, calculus, differential equations, probability, and standard machine learning. To build this understanding, my suggestion is to take the following courses (or similar) first:
+ [ME 581 (Numerical Methods in Mechanical Engineering)](https://engineering.purdue.edu/online/courses/numerical-methods-mechanical-engineering), and
+ [ME 539 (Introduction to Scientific Machine Learning)](https://predictivesciencelab.github.io/data-analytics-se/index.html).
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