#037 - Tour De Bayesian with Connor Tann

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