This newly-revamped, 4-unit, graduate-level class is positioned at the intersection of two exciting and rapidly-growing fields, namely space weather and machine learning. We will be reading through the newly-published book “Machine Learning Techniques for Space Weather”, and each week, a different student will be presenting one of the chapters in detail, thus giving the class an up to date overview of the various researchers around the world involved in this field, the problems they address, and the techniques they use. We will have two guest lectures including a former space physicist now working as a data scientist in industry, giving us the “industry perspective”, and another on (mostly Python-based) tools, libraries, workflows, and data repositories available to the heliophysics community. No machine learning or coding experience is necessary, all algorithms will be presented at a conceptual level. All are welcome!