Talk Abstract
At the heart of any machine learning (ML) problem is the identification of models that explain the data well, where learning about the model parameters, treated as random variables, is integral. Bayes’ theorem, and in general Bayesian learning, offers a principled framework to update one’s beliefs about an unknown quantity; Bayesian methods therefore play an important role in many aspects of ML. This introductory talk aims to highlight some of the most prominent areas in Bayesian ML from the perspective of statisticians and analysts, drawing parallels between these areas and common problems that Bayesian statisticians work on.
| Quan Nguyen | GitHub KrisNguyen135 |
Personal website |
Talk
Quan Nguyen
Quan is a Bayesian statistics enthusiast (and a programmer at heart). He is the author of several programming books on Python and scientific programming. Quan is currently pursuing a Ph.D. in computer science at Washington University in St. Louis, researching Bayesian methods in machine learning.
This is a PyMCon 2020 talk
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