(ML 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
From playlist Machine Learning
The Nuclear Shell Model: An Introduction
A basic introduction to the shell model to explain magic numbers in nuclei.
From playlist Nuclear Physics
The Incorrect Assumptions of the Ideal Gas Model - and Why It Still Works!
What exactly IS an Ideal Gas? And why do physicists use this model to represent real gases? In this video we'll compare the assumptions made by the ideal gas model with the properties of real gases, as well as how we can improve the ideal gas law in certain scenarios. As with every model
From playlist Thermodynamics by Parth G
Thermodynamics Chapter 2_5 Kinetic Theory of Gases.wmv
Kinetic theory of gases.
From playlist PHY1506
The Atom A3 The Bohr Model of the Hydrogen Atom
The Bohr model of the atom.
From playlist Physics - The Atom
PHY_024 - Linguistic Micro-Lectures: The Phoneme
In this micro-lecture of less than two minutes, Prof. Handke discusses the historical perspective and the various approaches towards the phoneme including their major differences. (Optional Spanish subtitles by Andrea Yaques, Lima, Peru)
From playlist Micro-Lectures - Phonology
Thermodynamics Chapter 2_6 Kinetic Theory of Gases.wmv
Kinetic theory of gases.
From playlist PHY1506
(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
The Atom A5 The Bohr Model of the Hydrogen Atom
The Bohr model of the atom.
From playlist Physics - The Atom
Thoughts, Thinking, & Thinkers (Tim Crane - 2017 Frege Lectures)
Professor Tim Crane gives a series of talks called "Thoughts, Thinking, & Thinkers" as part of the 2017 Frege Lectures in theoretical philosophy at the University of Tartu. Note, this is a re-upload. One of Frege’s most famous principles was ‘always to separate sharply the psychological
From playlist Philosophy of Mind
Novel and model-independent test of theories beyond GR - I. Saltas - Workshop 1 - CEB T3 2018
Ippocratis Saltas (Czech Academy of Sciences) / 19.09.2018 Novel and model-independent test of theories beyond GR ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
Tyson Littenberg - Building flexible, but not too flexible, models of gravitational wave data
Recorded 15 November 2021. Tyson Littenberg of the NASA - Marshall Space Flight Center presents "Building flexible, but not too flexible, models of gravitational wave data" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstract: Gravitat
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
The Cortical Predicament - J. N. Findlay on Consciousness (1971)
John Niemeyer Findlay gives a 1971 lecture on consciousness. J.N. Findlay was Clark Professor of Moral Philosophy and Metaphysics at Yale University, who studied under Rudolph Carnap and Martin Heidegger. Through analysis of Brentano, Merleau-Ponty, Hegel, Husserl, Heidegger, and Sartre, F
From playlist Philosophy of Mind
Juan Calderon-Bustillo - Challenge of characterising high-mass compact mergers: the case of GW190521
Recorded 18 November 2021. Juan Calderon-Bustillo of the University of Santiago de Compostela presents "The challenge of characterising high-mass compact mergers: the case of GW190521" at IPAM's Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy. Abstra
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
MIT 8.851 Effective Field Theory, Spring 2013 View the complete course: http://ocw.mit.edu/8-851S13 Instructor: Iain Stewart In this lecture, the professor discussed linear sigma model & field redefinitions, non-linear Lagrangian & symmetry breaking, Spurion analysis, and started topic of
From playlist MIT 8.851 Effective Field Theory, Spring 2013
Energy spectra and fluxes of buoyancy-driven flows by Abhishek Kumar
Summer school and Discussion Meeting on Buoyancy-driven flows DATE: 12 June 2017 to 20 June 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru Buoyancy plays a major role in the dynamics of atmosphere and interiors of planets and stars, as well as in engineering applications. This field
From playlist Summer school and Discussion Meeting on Buoyancy-driven flows
Dynamics of waves and vortices in the ocean by Jim Thomas
ICTS Seminar Title : Dynamics of waves and vortices in the ocean Speaker : Jim Thomas (University of North Carolina at Chapel Hill, United States) Date : Monday, 2nd November,
From playlist Seminar Series
Being in the World: A Tribute to Hubert Dreyfus | Episode 1809 | Closer To Truth
Hubert Dreyfus, renowned philosopher of phenomenology, died in 2017. A few years earlier, we discussed consciousness, A.I., God, creation, religion, body and soul, existence, and the meaning of life. A tribute to Hubert L. Dreyfus. Season 18, Episode 9 - #CloserToTruth ▶Register for fre
From playlist Closer To Truth | Season 18
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
Michael Mahoney: "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks"
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks" Michael Mahoney, University of California, Berkeley (UC Berkeley) Abstract: Physics has a
From playlist Machine Learning for Physics and the Physics of Learning 2019