Post Bayesian Machine Learning

Summary

In this talk, I provide my perspective on the Abstract: This talk will serve two purposes. In the first half, I will explain why this seminar series exists, and how it is organised. Abstract: We review the historical motivation in the development of general Jeremias Knoblauch, Associate Professor at UCL's Department of Statistical Science, provides his perspective on the For more information about Stanford's Neural networks are the backbone of deep Andrew G. Wilson teaches us what it means to adopt a When most people want to learn about Naive PyData Warsaw 2018 We will show how to assess the uncertainty of deep neural networks. We will cover Perhaps the most important formula in probability. Help fund future projects: patreon.com/3blue1brown An equally ... MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: ocw.mit.edu/RES-6-012S18 Instructor: ... MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: ocw.mit.edu/18-650F16 Instructor: Philippe ...