Elevator Pitch
Preparing an ML course for grad students can be challenging. By the time you finish preparing a class, a new paper may popup overturning your results! The books to choose may be even harder. The chapters can be rewritten quite a bit in the next edition of the book, while you are teaching the course.
Description
Preparing a Python ML course for grad students can be challenging. By the time you finish preparing a class, a new paper may popup overturning your results! The books to choose may be even harder. The chapters can be rewritten quite a bit in the next edition of the book, while you are teaching the course. So where do we start? Fundamentals - but are they really practical for solving real-world problems? Can ML teachers really compete with online courses, some available for free? Let’s try to navigate through this storm of machine learning books and frameworks in this class! In this talk you will learn how to control your environment for teaching the course using Jupyter Notebooks in the cloud, selecting great resources, and preparing materials for the course.
Notes
Follow the course here:
https://notebooks.azure.com/denfromufa/libraries/pmlc