I’ve been interested in Machine Learning since I was an undergraduate. Most of the ML I learned during my Master’s was covariance methods (kriging / Gaussian Processes), along with other supervised methods. Towards the end of my Ph.D., I started to learn more about generative models as part of Mike Mozers class on Probabilistic Models of Human and Machine Intelligence. I find myself returning more and more to many of the core concepts-- inference, graphical models, and the wider world of markov processes.
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Gaussian Processes
I presented a version of this notebook on Tuesday, April 29th to Fernando Perez’s research group at UC-Berkely as part of an ongoing biweekly collaboration for cross disciplinary open science. I’ve modified it slightly to:
Inference using Bayesian Networks
Note: This notebook was originally developed as part of the ICESat-2 2020 Hackweek Machine Learning tutorial (Instructors: Yara Mohajerani and Shane Grigsby). A recording of the tutorial is available here. The original source repository is at ICESAT