Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)
This video explains how to determine the kernel of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Linear Transformation: Which Vectors are in the Range of T and the Kernel of T?
This video explains how to determine if a given vector in the range / image and the kernel of linear transformation.
From playlist Kernel and Image of Linear Transformation
Calculating dimension and basis of range and kernel
German version here: https://youtu.be/lBdwtUa_BGM Support the channel on Steady: https://steadyhq.com/en/brightsideofmaths Official supporters in this month: - Petar Djurkovic - Lukas Mührke Here, I explain the typical calculation scheme for getting dimension and basis for the image/ran
From playlist Linear algebra (English)
Maximum Likelihood Estimation Examples
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Three examples of applying the maximum likelihood criterion to find an estimator: 1) Mean and variance of an iid Gaussian, 2) Linear signal model in
From playlist Estimation and Detection Theory
Determine a Basis for the Kernel of a Matrix Transformation (3 by 4)
This video explains how to determine a basis for the kernel of a matrix transformation.
From playlist Kernel and Image of Linear Transformation
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
Probability Density Function With Example | Probability And Statistics Tutorial | Simplilearn
🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=ProbabilityDensityFunction-4FP6B5SrqKw&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-sc
Score estimation with infinite-dimensional exponential families – Dougal Sutherland, UCL
Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can
From playlist Approximating high dimensional functions
Seminar In the Analysis and Methods of PDE (SIAM PDE): Luis Silvestre
Title: Gaussian lower bounds for the Boltzmann equation without cut-off Date: July 1, 2021, 11:30 am ET Speaker: Luis Silvestre, University of Chicago Abstract: The Boltzmann equation models the evolution of densities of particles in a gas. Its global well posedness is a major open proble
From playlist Seminar In the Analysis and Methods of PDE (SIAM PDE)
Krithika Manohar: "Kernel Analog Forecasting: Multiscale Problems"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Kernel Analog Forecasting: Multiscale Problems" Krithika Manohar - California Institute of Technology,
From playlist Machine Learning for Physics and the Physics of Learning 2019
From playlist Plenary talks One World Symposium 2020
(ML 3.7) The Big Picture (part 3)
How the core concepts and methods in machine learning arise naturally in the course of solving the decision theory problem. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Robert Seiringer: The local density approximation in density functional theory
We present a mathematically rigorous justification of the Local Density Approximation in density functional theory. We provide a quantitative estimate on the difference between the grand-canonical Levy-Lieb energy of a given density (the lowest possible energy of all quantum st
From playlist Mathematical Physics
Find the Kernel of a Matrix Transformation (Give Direction Vector)
This video explains how to determine direction vector a line that represents for the kernel of a matrix transformation
From playlist Kernel and Image of Linear Transformation
Gabriele Steidl: Stochastic normalizing flows and the power of patches in inverse problems
CONFERENCE Recording during the thematic meeting : "Learning and Optimization in Luminy" the October 4, 2022 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on C
From playlist Probability and Statistics
Complex Stochastic Models and their Applications by Subhroshekhar Ghosh
PROGRAM: TOPICS IN HIGH DIMENSIONAL PROBABILITY ORGANIZERS: Anirban Basak (ICTS-TIFR, India) and Riddhipratim Basu (ICTS-TIFR, India) DATE & TIME: 02 January 2023 to 13 January 2023 VENUE: Ramanujan Lecture Hall This program will focus on several interconnected themes in modern probab
From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY
Lewis Marsh (8/3/20): Geometric and topological data analysis of enzyme kinetics
Title: Geometric and topological data analysis of enzyme kinetics Abstract: In this talk, we will mathematically study a differential equation model and generated data describing molecular dynamics of Extracellular Signal Regulated Kinase (ERK), which is known to be linked to human cancer
From playlist ATMCS/AATRN 2020
The Bergman kernel of the polydisk and the ball
I compute the Bergman kernel of the unit polydisk and the unit Euclidean ball. For my previous video on the Bergman kernel see https://www.youtube.com/watch?v=loIC28LNgNM
From playlist Several Complex Variables
Functional Data Analysis Under Shape Constraints - Srivastava - Workshop 2 - CEB T1 2019
Anuj Srivastava (Florida state Univ.) / 13.03.2019 Functional Data Analysis Under Shape Constraints. (Joint work with Sutanoy Dasgupta, Ian Jermyn, and Debdeep Pati). We consider a subarea of functional data analysis, where functions of interest are constrained to have pre-determined sh
From playlist 2019 - T1 - The Mathematics of Imaging