Math 060 Fall 2017 111317C Orthonormal Bases
Motivation: how to obtain the coordinate vector with respect to a given basis? Definition: orthogonal set. Example. Orthogonal implies linearly independent. Orthonormal sets. Example of an orthonormal set. Definition: orthonormal basis. Properties of orthonormal bases. Example: Fou
From playlist Course 4: Linear Algebra (Fall 2017)
Orthogonal and Orthonormal Sets of Vectors
This video defines orthogonal and orthonormal sets of vectors.
From playlist Orthogonal and Orthonormal Sets of Vectors
Linear Algebra: Given an orthonormal basis of R^n, we present a quick method for finding coefficients of linear combination in terms of the basis. We also give an analogue of Parseval's Identity, which relates these coefficients to the squared length of the vector.
From playlist MathDoctorBob: Linear Algebra I: From Linear Equations to Eigenspaces | CosmoLearning.org Mathematics
Orthogonality and Orthonormality
We know that the word orthogonal is kind of like the word perpendicular. It implies that two vectors have an angle of ninety degrees or half pi radians between them. But this term means much more than this, as we can have orthogonal matrices, or entire subspaces that are orthogonal to one
From playlist Mathematics (All Of It)
Linear Algebra: Orthonormal Basis
Learn the basics of Linear Algebra with this series from the Worldwide Center of Mathematics. Find more math tutoring and lecture videos on our channel or at http://centerofmath.org/ More on unit vectors: https://www.youtube.com/watch?v=C6EYJVBYXIo
From playlist Basics: Linear Algebra
Linear Algebra - Lecture 39 - Orthonormal Sets
In this lecture, we discuss orthonormal sets of vectors. We investigate matrices with orthonormal columns. We also define an orthogonal matrix.
From playlist Linear Algebra Lectures
Orthonormal bases. The Gram-Schmidt Procedure. Schuur's Theorem on upper-triangular matrix with respect to an orthonormal basis. The Riesz Representation Theorem.
From playlist Linear Algebra Done Right
Find the B-Coordinates of a Vector in a Subspace with an Orthogonal Basis
This video explains how to determine the B-coordinates of a vector in a subspace of R2 with an orthogonal basis
From playlist Orthogonal and Orthonormal Sets of Vectors
Math 060 Fall 2017 111517C Orthonormal Bases, Orthogonal Matrices, and Method of Least Squares
Definition of orthogonal matrices. Example: rotation matrix. Properties: Q orthogonal if and only if its transpose is its inverse. Q orthogonal implies it is an isometry; that it is isogonal (preserves angles). Theorem: How to find, given a vector in an inner product space, the closest
From playlist Course 4: Linear Algebra (Fall 2017)
Lecture 01-03 Linear Algebra review
Machine Learning by Andrew Ng [Coursera] 0113 Matrices and vectors 0114 Addition and scalar multiplication 0115 Matrix-vector multiplication 0116 Matrix-matrix multiplication 0117 Matrix multiplication properties 0118 Inverse and transpose
From playlist Machine Learning by Professor Andrew Ng
The Diagonalization of Matrices
This video explains the process of diagonalization of a matrix.
From playlist The Diagonalization of Matrices
Part IV: Matrix Algebra, Lec 2 | MIT Calculus Revisited: Multivariable Calculus
Part IV: Matrix Algebra, Lecture 2: The "Game" of Matrices Instructor: Herbert Gross View the complete course: http://ocw.mit.edu/RES18-007F11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Calculus Revisited: Multivariable Calculus
Desmos Matrix Calc: Matrix Multiplication
This video explains how to us the Desmos Matrix Calculator to perform matrix multiplication. Not solved by hand
From playlist Introduction to Matrices and Matrix Operations
Eigenvectors and Eigenvalues with Jon Krohn
Data scientist Jon Krohn introduces the linear algebra concepts of Eigenvectors and Eigenvalues with a focus on Machine Learning and Python programming. This lesson is an excerpt from “Linear Algebra for Machine Learning LiveLessons” Purchase the entire video course at informit.com/youtub
From playlist Talks and Tutorials
We start discussing how to label matrices and their elements. We then define Order of Matrices and Equal Matrices working an example at 5:49. I then discuss Adding Matrices at 9:40 and work through three examples. Properties of Adding Matrices are explained at 16:00 Scalar Multiplication
From playlist Linear Algebra
Using a Matrix Equation to Solve a System of Equations
This video shows how to solve a system of equations by using a matrix equation. The graphing calculator is integrated into the lesson. http://mathispower4u.yolasite.com/ http://mathispower4u.wordpress.com/
From playlist Matrix Equations
This video defines the transpose of a matrix and explains how to transpose a matrix. The properties of transposed matrices are also discussed. Site: mathispower4u.com Blog: mathispower4u.wordpress.com
From playlist Introduction to Matrices and Matrix Operations
WildLinAlg12: Generalized dilations and eigenvectors
This video introduces the important idea of changing coordinates in Linear Algebra. A linear transformation can be described using many different matrices, depending on the underlying coordinate system, or ordered basis, which is used to describe the space. The simplest case is when the
From playlist A first course in Linear Algebra - N J Wildberger
Singular Value Decomposition (SVD) and Image Compression
Github repo: http://www.github.com/luisguiserrano/singular_value_decomposition Grokking Machine Learning Book: https://www.manning.com/books/grokking-machine-learning 40% discount promo code: serranoyt In this video, we learn a very useful matrix trick called singular value decomposition
From playlist Unsupervised Learning
Linear Algebra 22b: Orthoscaling Transformations Are (Sometimes) Represented by Symmetric 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