(ML 19.2) Existence of Gaussian processes
Statement of the theorem on existence of Gaussian processes, and an explanation of what it is saying.
From playlist Machine Learning
(ML 19.1) Gaussian processes - definition and first examples
Definition of a Gaussian process. Elementary examples of Gaussian processes.
From playlist Machine Learning
Approximating Functions in a Metric Space
Approximations are common in many areas of mathematics from Taylor series to machine learning. In this video, we will define what is meant by a best approximation and prove that a best approximation exists in a metric space. Chapters 0:00 - Examples of Approximation 0:46 - Best Aproximati
From playlist Approximation Theory
Gaussian Integral 7 Wallis Way
Welcome to the awesome 12-part series on the Gaussian integral. In this series of videos, I calculate the Gaussian integral in 12 different ways. Which method is the best? Watch and find out! In this video, I calculate the Gaussian integral by using a technique that is very similar to the
From playlist Gaussian Integral
(ML 19.3) Examples of Gaussian processes (part 1)
Illustrative examples of several Gaussian processes, and visualization of samples drawn from these Gaussian processes. (Random planes, Brownian motion, squared exponential GP, Ornstein-Uhlenbeck, a periodic GP, and a symmetric GP).
From playlist Machine Learning
(ML 19.4) Examples of Gaussian processes (part 2)
Illustrative examples of several Gaussian processes, and visualization of samples drawn from these Gaussian processes. (Random planes, Brownian motion, squared exponential GP, Ornstein-Uhlenbeck, a periodic GP, and a symmetric GP).
From playlist Machine Learning
(ML 16.7) EM for the Gaussian mixture model (part 1)
Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.
From playlist Machine Learning
Gaussian Integral 10 Fourier Way
Welcome to the awesome 12-part series on the Gaussian integral. In this series of videos, I calculate the Gaussian integral in 12 different ways. Which method is the best? Watch and find out! In this video, I show how the Gaussian integral appears in the Fourier transform: Namely if you t
From playlist Gaussian Integral
Gaussian Integral 8 Original Way
Welcome to the awesome 12-part series on the Gaussian integral. In this series of videos, I calculate the Gaussian integral in 12 different ways. Which method is the best? Watch and find out! In this video, I present the classical way using polar coordinates, the one that Laplace original
From playlist Gaussian Integral
From playlist COMP0168 (2020/21)
Slides and more information: https://mml-book.github.io/slopes-expectations.html
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
Slides and more information: https://mml-book.github.io/slopes-expectations.html
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
From playlist COMP0168 (2020/21)
ML Tutorial: Gaussian Processes (Richard Turner)
Machine Learning Tutorial at Imperial College London: Gaussian Processes Richard Turner (University of Cambridge) November 23, 2016
From playlist Machine Learning Tutorials
ML Tutorial: Probabilistic Numerical Methods (Jon Cockayne)
Machine Learning Tutorial at Imperial College London: Probabilistic Numerical Methods Jon Cockayne (University of Warwick) February 22, 2017
From playlist Machine Learning Tutorials
Maxim Raginsky: "A mean-field theory of lazy training in two-layer neural nets"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning "A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics" Maxim Raginsky - University of Illinois at Urbana-Cham
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Pathwise Conditioning and Non-Euclidean Gaussian Processes
A Google TechTalk, presented by Alexander Terenin, 2022-11-28 BayesOpt Speaker Series. ABSTRACT: In Gaussian processes, conditioning and computation of posterior distributions is usually done in a distributional fashion by working with finite-dimensional marginals. However, there is anoth
From playlist Google BayesOpt Speaker Series 2021-2022
Bayesian optimisation for likelihood-free cosmological (...) - Leclercq - Workshop 2 - CEB T3 2018
Leclercq (Imperial College) / 22.10.2018 Bayesian optimisation for likelihood-free cosmological inference ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
From playlist COMP0168 (2020/21)
PUSHING A GAUSSIAN TO THE LIMIT
Integrating a gaussian is everyones favorite party trick. But it can be used to describe something else. Link to gaussian integral: https://www.youtube.com/watch?v=mcar5MDMd_A Link to my Skype Tutoring site: dotsontutoring.simplybook.me or email dotsontutoring@gmail.com if you have ques
From playlist Math/Derivation Videos