This course is meant for beginning machine learning practitioners. It is an asynchronous course that can be started at any time and taken at any pace; the "start date" above only refers to the first day that the materials were made available. It will be helpful to be familiar with Python and Jupyter notebooks, since this is what we use for implementation. We provide a foundation in methods of Machine Learning, and focus on its applications to real research examples, from exploratory data analysis to hypothesis testing and diagnostics. We draw most examples from Physics and Astronomy.

Our hope is that at the end of the class, participants will:

- Be able to read and understand a paper that uses ML;
- Learn how to build, diagnose, and optimize a ML model;
- Get a sense of what methods are available, and match them to research problems;
- Have draft notebooks with simple implementations to use as a foundation for writing more (and better) code.