(ML 11.8) Bayesian decision theory
Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.
From playlist Machine Learning
(ML 7.1) Bayesian inference - A simple example
Illustration of the main idea of Bayesian inference, in the simple case of a univariate Gaussian with a Gaussian prior on the mean (and known variances).
From playlist Machine Learning
(ML 12.4) Bayesian model selection
Approaches to model selection from a Bayesian perspective: Bayesian model averaging (BMA), "Type II MAP", and Type II Maximum Likelihood (a.k.a. ML-II, a.k.a. the evidence approximation, a.k.a. empirical Bayes).
From playlist Machine Learning
(ML 11.4) Choosing a decision rule - Bayesian and frequentist
Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.
From playlist Machine Learning
Lecture 9D : Introduction to the Bayesian Approach
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 9D : Introduction to the Bayesian Approach
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Christine Keribin: Variational Bayes methods and algorithms - Part 1
Abstract: Bayesian posterior distributions can be numerically intractable, even by the means of Markov Chain Monte Carlo methods. Bayesian variational methods can then be used to compute directly (and fast) a deterministic approximation of these posterior distributions. In this course, I d
From playlist Probability and Statistics
From playlist COMP0168 (2020/21)
ML Tutorial: Adversarial and Competitive Methods in Machine Learning (Amos Storkey)
Machine Learning Tutorial at Imperial College London: Adversarial and Competitive Methods in Machine Learning Amos Storkey (University of Edinburgh) October 28, 2015
From playlist Machine Learning Tutorials
From playlist COMP0168 (2020/21)
Peter Frazier: "Accelerating Scientific Discovery through Interpretable Machine Learning and Int..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Accelerating Scientific Discovery through Interpretable Machine Learning and Intelligent Experimentation" Peter Frazier, Cornell University Abstract: Historically, the
From playlist Machine Learning for Physics and the Physics of Learning 2019
The Unreasonable Effectiveness of Bayesian Prediction
My Patreon : https://www.patreon.com/user?u=49277905 Icon References : https://www.flaticon.com/authors/srip
From playlist Bayesian Statistics
Uncertainty in Visuomotor Behavior by Konrad Kording
PROGRAM ICTP-ICTS WINTER SCHOOL ON QUANTITATIVE SYSTEMS BIOLOGY (ONLINE) ORGANIZERS: Vijaykumar Krishnamurthy (ICTS-TIFR, India), Venkatesh N. Murthy (Harvard University, USA), Sharad Ramanathan (Harvard University, USA), Sanjay Sane (NCBS-TIFR, India) and Vatsala Thirumalai (NCBS-TIFR,
From playlist ICTP-ICTS Winter School on Quantitative Systems Biology (ONLINE)
Sudipto Banerjee: High-dimensional Bayesian geostatistics
Abstract: With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarc
From playlist Probability and Statistics
Rémi Bardenet: A tutorial on Bayesian machine learning: what, why and how - lecture 1
HYBRID EVENT Recorded during the meeting "End-to-end Bayesian Learning Methods " the October 25, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's
From playlist Mathematical Aspects of Computer Science
Benjamin Guedj: On generalisation and learning
A (condensed) primer on PAC-Bayes, followed by News from the PAC-Bayes frontline. LMS Computer Science Colloquium 2021
From playlist LMS Computer Science Colloquium Nov 2021
Bayesian Linear Regression : Data Science Concepts
The crazy link between Bayes Theorem, Linear Regression, LASSO, and Ridge! LASSO Video : https://www.youtube.com/watch?v=jbwSCwoT51M Ridge Video : https://www.youtube.com/watch?v=5asL5Eq2x0A Intro to Bayesian Stats Video : https://www.youtube.com/watch?v=-1dYY43DRMA My Patreon : https:
From playlist Bayesian Statistics
Marginal-based Methods for Differentially Private Synthetic Data
A Google TechTalk, presented by Ryan McKenna, 2021/12/08 Differential Privacy for ML series.
From playlist Differential Privacy for ML
Chaitanya Swamy: Signaling in Bayesian Games
We study the optimization problem faced by an informed principal in a Bayesian game, who can reveal some information about the underlying random state of nature to the players (thereby influencing their payoffs) so as to obtain a desirable equilibrium. This yields the following signaling p
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"
An introduction to the use of Bayes' rule in statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortunately, Ox Educ is no more. Don't fret however as a whol
From playlist Bayesian statistics: a comprehensive course