Linear Algebra 16h6: Generalized Eigenvectors
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From playlist Part 3 Linear Algebra: Linear Transformations
Linear Algebra 16h4: Defective Matrices
https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep
From playlist Part 3 Linear Algebra: Linear Transformations
Linear Algebra 11f: An Example of an Incompatible Matrix Product
https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep
From playlist Part 1 Linear Algebra: An In-Depth Introduction with a Focus on Applications
What is a matrix? Free ebook http://tinyurl.com/EngMathYT
From playlist Intro to Matrices
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
2 Construction of a Matrix-YouTube sharing.mov
This video shows you how a matrix is constructed from a set of linear equations. It helps you understand where the various elements in a matrix comes from.
From playlist Linear Algebra
Null space of a matrix example
In today's lecture I work through an example to show you a well-known pitfall when it comes to the null space of a matrix. In the example I show you how to create the special cases and how to use them to represent the null space. There is also a quick look at the NullSpace function in Ma
From playlist Introducing linear algebra
How do we add matrices. A matrix is an abstract object that exists in its own right, and in this sense, it is similar to a natural number, or a complex number, or even a polynomial. Each element in a matrix has an address by way of the row in which it is and the column in which it is. Y
From playlist Introducing linear algebra
The Confusion Matrix : Data Science Basics
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From playlist Data Science Basics
Ilka Brunner - Truncated Affine Rozansky-Witten Models as Extended TQFTs
Mathematicians formulate fully extended d-dimensional TQFTs in terms of functors between a higher category of bordism and suitable target categories. Furthermore, the cobordism hypothesis identifies the basic building blocks of such TQFTs. In this talk, I will discuss Rozansky Witten model
From playlist Mikefest: A conference in honor of Michael Douglas' 60th birthday
Linear Algebra 16n: Every Matrix Satisfies Its Characteristic Equation
https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep
From playlist Part 3 Linear Algebra: Linear Transformations
Null Space and Column Space of a Matrix
Given a matrix A(ie a linear transformation) there are several important related subspaces. In this video we investigate the Nullspace of A and the column space of A. The null space is the vectors that are "killed" by the transformation - ie sent to zero. The column space will be the image
From playlist Older Linear Algebra Videos
Linear Algebra 16h5: Defective Transformations?
https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep
From playlist Part 3 Linear Algebra: Linear Transformations
Similarity Transformation and Diagonalization
In this video we investigate similarity transformations in the context of linear algebra. We show how the similarity transformation can be used to transform a square matrix into another square matrix that shares properties with the original matrix. In particular, the determinant, eigenva
From playlist Linear Algebra
Nexus Trimester - Sidharth Jaggi (The Chinese University of Hong Kong)
Group-testing: Together we are one Sidharth Jaggi (The Chinese University of Hong Kong) March 16, 2016 Group testing is perhaps the “simplest” class of non-linear inference problems. Broadly speaking, group-testing measurements exhibit a “threshold” behaviour, with positive test outcomes
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
Integrable Field Theories from 4d Chern-Simons Theory (Remote Talk) by Masahito Yamazaki
PROGRAM: INTEGRABLE SYSTEMS IN MATHEMATICS, CONDENSED MATTER AND STATISTICAL PHYSICS ORGANIZERS: Alexander Abanov, Rukmini Dey, Fabian Essler, Manas Kulkarni, Joel Moore, Vishal Vasan and Paul Wiegmann DATE : 16 July 2018 to 10 August 2018 VENUE: Ramanujan Lecture Hall, ICTS Bangalore
From playlist Integrable systems in Mathematics, Condensed Matter and Statistical Physics
(3.7.102) Solve a Linear System of ODEs using the Eigenvalue Method: Repeated Eigenvalues, 1 Defect
This video explains how to solve the system x'=Ax when matrix A has repeated eigenvalues and one eigenvalue has 1 defect. https://mathispower4u.com
From playlist Differential Equations: Complete Set of Course Videos
From playlist Unlisted LA Videos