Bayesian 3D Priors for Brain Imaging, Per Sidén - Bayes@Lund 2018
Find more info about Bayes@Lund, including slides, here: https://bayesat.github.io/lund2018/bayes_at_lund_2018.html
From playlist Bayes@Lund 2018
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]
(ML 7.2) Aspects of Bayesian inference
An informal overview of Bayesian inference, Bayesian procedures, Objective versus Subjective Bayes, Pros/Cons of a Bayesian approach, and priors.
From playlist Machine Learning
(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
Mapping The Brain | Digging Deeper
Should the United States spend billions to completely map the human brain? Will it ever be possible to build an artificial brain - and, if we do, what are the implications for the future? Join Ben and Matt as they talk about some interesting stuff that didn't make it into the Deceptive Bra
From playlist Stuff They Don't Want You To Know, New Episodes!
(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 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
Statistical Rethinking 2023 - 18 - Missing Data
Course: https://github.com/rmcelreath/stat_rethinking_2023 Intro music: https://www.youtube.com/watch?v=RSsstXfcRWw Icebear music: https://www.youtube.com/watch?v=0h9tC3FM9UI Outline 00:00 Introduction 05:18 Missing data in DAGs 19:42 Bayesian imputation part 1 33:34 Pause 34:30 Bayesian
From playlist Statistical Rethinking 2023
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)
Research Methods of Biopsychology
With some information regarding the organization of neurons and neural pathways, we are ready to start getting into some deeper topics. But before we do that, it will be useful to get a general sense of precisely how we learn about the things we will be discussing. The brain is complicated
From playlist Biopsychology
Probability theory and AI | The Royal Society
Join Professor Zoubin Ghahramani to explore the foundations of probabilistic AI and how it relates to deep learning. 🔔Subscribe to our channel for exciting science videos and live events, many hosted by Brian Cox, our Professor for Public Engagement: https://bit.ly/3fQIFXB #Probability #A
From playlist Latest talks and lectures
Bayesian Modeling of Behavior (Tutorial) 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)
Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Josh Tenenbaum Exploring how humans learn new concepts and make intelligent inferences from little experience. Using probabilistic generative models
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
Marcelo Pereyra: Bayesian inference and mathematical imaging - Lecture 3
Bayesian inference and mathematical imaging - Part 3: probability and convex optimisation Abstract: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framework underpin
From playlist Probability and Statistics
The Master Algorithm | Pedro Domingos | Talks at Google
Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more—to cure cancer and AIDS and possibly solve ev
From playlist AI talks
Building Machines that Learn & Think Like People - Prof. Josh Tenenbaum ICML2018
Recorded July 13th, 2018 at the 2018 International Conference on Machine Learning Joshua Tenenbaum is Professor of Cognitive Science and Computation at the Massachusetts Institute of Technology. He is known for contributions to mathematical psychology and Bayesian cognitive science. htt
From playlist AI talks
Computational Principles of Sensorimotor Control (Lecture 1) by Daniel Wolpert
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)
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
Bayesian inference and convex geometry: theory, methods, (...) - Pereyra - Workshop 2 - CEB T1 2019
Marcelo Pereyra (Heriot-Watt Univ.) / 14.03.2019 Bayesian inference and convex geometry: theory, methods, and algorithms. This talk summarises some new developments in theory, methods, and algorithms for performing Bayesian inference in high-dimensional models that are log-concave, with
From playlist 2019 - T1 - The Mathematics of Imaging