Machine Learning Street Talk (MLST) artwork

#037 - Tour De Bayesian with Connor Tann

Machine Learning Street Talk (MLST)

English - January 11, 2021 01:30 - 1 hour - 87.6 MB
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Previous Episode: #036 - Max Welling: Quantum, Manifolds & Symmetries in ML
Next Episode: #038 - Professor Kenneth Stanley - Why Greatness Cannot Be Planned

Connor Tan is a physicist and senior data scientist working for a multinational energy company where he co-founded and leads a data science team. He holds a first-class degree in experimental and theoretical physics from Cambridge university. With a master's in particle astrophysics. He specializes in the application of machine learning models and Bayesian methods. Today we explore the history, pratical utility, and unique capabilities of Bayesian methods. We also discuss the computational difficulties inherent in Bayesian methods along with modern methods for approximate solutions such as Markov Chain Monte Carlo. Finally, we discuss how Bayesian optimization in the context of automl may one day put Data Scientists like Connor out of work.




Panel: Dr. Keith Duggar, Alex Stenlake, Dr. Tim Scarfe




00:00:00 Duggars philisophical ramblings on Bayesianism


00:05:10 Introduction


00:07:30 small datasets and prior scientific knowledge


00:10:37 Bayesian methods are probability theory


00:14:00 Bayesian methods demand hard computations


00:15:46 uncertainty can matter more than estimators


00:19:29 updating or combining knowledge is a key feature


00:25:39 Frequency or Reasonable Expectation as the Primary Concept 


00:30:02 Gambling and coin flips


00:37:32 Rev. Thomas Bayes's pool table


00:40:37 ignorance priors are beautiful yet hard


00:43:49 connections between common distributions


00:49:13 A curious Universe, Benford's Law


00:55:17 choosing priors, a tale of two factories


01:02:19 integration, the computational Achilles heel


01:35:25 Bayesian social context in the ML community


01:10:24 frequentist methods as a first approximation


01:13:13 driven to Bayesian methods by small sample size


01:18:46 Bayesian optimization with automl, a job killer?


01:25:28 different approaches to hyper-parameter optimization


01:30:18 advice for aspiring Bayesians


01:33:59 who would connor interview next?




Connor Tann: https://www.linkedin.com/in/connor-tann-a92906a1/


https://twitter.com/connossor

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