Machine learning in Geoscience Seminar: syllabus and review
source link: https://andrewjmoodie.com/blog/2018/12-17-2018-machine-learning-seminar/
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Andrew J. Moodie
NSF Postdoc at UT Austin and Stanford, working in geoscience / data science nexus to develop sustainable delta management strategies.
Machine learning in Geoscience Seminar: syllabus and review
I led the organization of a “Machine Learning in Geosciences” seminar this fall (2018). I did not do it alone, I worked with my advisor Jeff Nittrouer, and Texas AM professor Ryan Ewing. My responsibilities included selection of reading material for the course; Jeff and Ryan handled the invited speakers and student presentations.
The seminar was not for credit, but we did have a healthy 10-12 students and faculty participate throughout the semester. Ultimately I think the seminar was a huge success; I learned an absolute ton about machine learning, and consider myself to be literate on the subject now. In fact, I’ve even begun to incorporate some deep learning into my research and have written a proposal for funding to continue to pursue this endeavor.
The course design was roughly as follows:
- four (4) weeks of background reading on machine learning tools, techniques, vocabulary
- four (4) weeks of primary scientific readings on various geoscience subjects
- three (3) weeks of invited presenters
- two (2) weeks of student project presentations
You can view the complete, detailed syllabus we created here: https://docs.google.com/document/d/1Bmq9m96jLT-WlDNlsAu_jYB6J-qg-Uxg0eg0FIg04Gs/edit?usp=sharing
This page contains a ton of resources.
Updated: December 17, 2018
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