Linear Algebra for Computer Scientists. 12. Introducing the Matrix
This computer science video is one of a series of lessons about linear algebra for computer scientists. This video introduces the concept of a matrix. A matrix is a rectangular or square, two dimensional array of numbers, symbols, or expressions. A matrix is also classed a second order
From playlist Linear Algebra for Computer Scientists
Matrix determinants & inverses | Appendix B | Vector Calculus for Engineers
The two-by-two and three-by-three determinant, and the inverse of an orthogonal matrix. Join me on Coursera: https://www.coursera.org/learn/vector-calculus-engineers Lecture notes at http://www.math.ust.hk/~machas/vector-calculus-for-engineers.pdf Subscribe to my channel: http://www.yo
From playlist Vector Calculus for Engineers
We have already looked at the column view of a matrix. In this video lecture I want to expand on this topic to show you that each matrix has a column space. If a matrix is part of a linear system then a linear combination of the columns creates a column space. The vector created by the
From playlist Introducing linear algebra
matrix choose a matrix. Calculating the number of matrix combinations of a matrix, using techniques from linear algebra like diagonalization, eigenvalues, eigenvectors. Special appearance by simultaneous diagonalizability and commuting matrices. In the end, I mention the general case using
From playlist Eigenvalues
Understanding Matrices and Matrix Notation
In order to do linear algebra, we will have to know how to use matrices. So what's a matrix? It's just an array of numbers listed in a grid of particular dimensions that can represent the coefficients and constants from a system of linear equations. They're fun, I promise! Let's just start
From playlist Mathematics (All Of It)
Deep Learning Lecture 7.4 - VAMPnet
Learning Slow Manifolds with Markovian methods - variational approach for Markov processes (VAMP) - VAMPnet
From playlist Deep Learning Lecture
In this very easy and short tutorial I explain the concept of the transpose of matrices, where we exchange rows for columns. The matrices have some properties that you should be aware of. These include how to the the transpose of the product of matrices and in the transpose of the invers
From playlist Introducing linear algebra
In this tutorial I put emphasis of the column view of a matrix of coefficients. We are used to the row view when it comes to systems of linear equations, but it is the column view that is much more fascinating. The column view helps us view a system of linear equations as vectors in a sp
From playlist Introducing linear algebra
Time Scales and Manifestations of Chaos in Many-Body Quantum Dynamics by Lea F. Santos
PROGRAM THERMALIZATION, MANY BODY LOCALIZATION AND HYDRODYNAMICS ORGANIZERS: Dmitry Abanin, Abhishek Dhar, François Huveneers, Takahiro Sagawa, Keiji Saito, Herbert Spohn and Hal Tasaki DATE : 11 November 2019 to 29 November 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore How do is
From playlist Thermalization, Many Body Localization And Hydrodynamics 2019
The Inverse of a 4 by 4 Matrix Given the Determinant and Cofactor Matrix
This video explains how to find the inverse matrix of a 4 by 4 matrix using the adjoint method given the determinant and the cofactor matrix.
From playlist Inverse Matrices
Lec 35 | MIT 5.80 Small-Molecule Spectroscopy and Dynamics, Fall 2008
Lecture 35: Wavepacket dynamics II Instructor: Robert Field View the complete course: http://ocw.mit.edu/5-80F08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 5.80 Small-Molecule Spectroscopy and Dynamics, Fall 2008
Random Processes and Stationarity
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduction to describing random processes using first and second moments (mean and autocorrelation/autocovariance). Definition of a stationa
From playlist Random Signal Characterization
Sarah Vigeland - Supermassive Black Holes and Merging Galaxies - IPAM at UCLA
Recorded 15 November 2021. Sarah Vigeland of the University of Wisconsin-Milwaukee presents "Supermassive Black Holes and Merging Galaxies: Low-Frequency Gravitational Wave Detection with Pulsar Timing Arrays" at IPAM's Workshop III: Source inference and parameter estimation in Gravitation
From playlist Workshop: Source inference and parameter estimation in Gravitational Wave Astronomy
Time Series class: Part 1 - Dr Ioannis Papastathopoulos, University of Edinburgh
Part 2: https://youtu.be/7n0HTtThMe0 Introduction: Moving average, Autoregressive and ARMA models. Parameter estimation, likelihood based inference and forecasting with time series. Advanced: State-space models (hidden Markov models, Kalman filter) and applications. Recurrent neural netw
From playlist Data science classes
Frequency-ranked data-driven stochastic modelling... - Chekroun - Workshop 2 - CEB T3 2019
Chekroun (UCLA, USA) / 13.11.2019 Frequency - ranked data - driven stochastic modelling, and applications ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ T
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Autoregressive Models: The Yule-Walker Equations
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. The Yule-Walker equations relate the auto covariance of a random signal to the autoregressive (AR) model parameters. They can be used to estimate A
From playlist Random Signal Characterization
Lecture 15 - ARIMA & GARCH Models
This is Lecture 15 of the COMP510 (Computational Finance) course taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Hong Kong University of Science and Technology in 2008. The lecture slides are available at: http://www.algorithm.cs.sunysb.edu/computationalfinance/pd
From playlist COMP510 - Computational Finance - 2007 HKUST
Matrix Addition, Subtraction, and Scalar Multiplication
This video shows how to add, subtract and perform scalar multiplication with matrices. http://mathispower4u.yolasite.com/ http://mathispower4u.wordpress.com/
From playlist Introduction to Matrices and Matrix Operations
Samir Chowdhury (8/17/20): Exploring brain dynamics during ongoing cognition using Mapper
Title: Exploring the landscape of brain dynamics during ongoing cognition using Mapper Abstract: Modern non-invasive neuroimaging methods such as fMRI capture high-resolution spatial patterns of brain activity. While most existing techniques for analyzing neuroimaging data study changes i
From playlist ATMCS/AATRN 2020
Visualization of tensors - part 1
This video visualizes tensors. It shows some introduction to tensor theory and demonstrates it with the Cauchy stress tensor. Future parts of this series will show more theory and more examples. It talks about the term 'tensor' as used in physics and math. In the field of AI the term 'te
From playlist Animated Physics Simulations