Using this book#
This JupyterBook is intended for a very short introduction to machine learning in physics and astronomy. It is not a comprehensive textbook, but rather a collection of notes and examples that can be used to get started with machine learning in these fields.
The in-class activities are part of an in-person course that are worked through in class. The activities are not intended to be self-contained, but rather to be used as a starting point for further exploration and discussion.
We are only attempting to introduce the basic concepts and techniques of machine learning, and to provide a foundation for further study. The principal goal is to provide an understanding of the scikit-learn library and how it can be used to solve problems in physics and astronomy. The workflow for using the scikit-learn library is introduced, and the basic concepts of supervised and unsupervised learning are discussed.
There are numerous links to external resources and additional reading material throughout the book. We have written some notes and resources with code examples that can be used to explore the topics in more depth. These resources are not intended to be comprehensive, but rather to provide a starting point for further exploration.
Recommended Practice#
We recommend coming to class and working through the in-class activities with your classmates. The activities are designed to be worked through in groups, and we encourage you to discuss the problems and solutions with your peers.
The additional notes and resources are intended to support you and provide you with some starter codes for the more advanced considerations. We encourage you to explore these resources and to use them to further your understanding of the topics covered in the book.
Of course, questions and discussions are always welcome. If you have any questions or comments about the book, please feel free to reach out to us. We are happy to help and to provide additional resources and support as needed.