The webinar will cover the following aspects:
(1) My story and how I transitioned to ML.
(2) The problems with current ML courses for engineers.
(3) How you can transition to ML from any engineering field.
(4) Introduction to the field of Scientific Machine Learning (SciML)
(5) The importance of ML research projects than toy Kaggle projects
(6) How to start doing research projects in the field of SciML?
(7) Converting projects into research: How to do independent research in ML and publish your first paper?
Who is this webinar for:
(a) Anyone who wants to transition to machine learning (ML) from an engineering field, but doesn't know the steps to follow.
(b) Anyone who wants to learn how to integrate domain knowledge i.e knowledge in your specific field, with ML.
(c) Anyone who wants to learn about the new field of Scientific Machine Learning.
(d) Anyone who wants to start doing ML research
(e) Anyone who wants to build their grad school profile, through an ML publication.