PDF4PRO ⚡AMP

Modern search engine that looking for books and documents around the web

Example: barber

Mathematics for Machine Learning - GitHub Pages

Mathematics for Machine Learning Garrett Thomas Department of Electrical Engineering and Computer Sciences University of California, Berkeley January 11, 2018. 1 About Machine Learning uses tools from a variety of mathematical fields. This document is an attempt to provide a summary of the mathematical background needed for an introductory class in Machine Learning , which at UC Berkeley is known as CS 189/289A. Our assumption is that the reader is already familiar with the basic concepts of multivariable calculus and linear algebra (at the level of UCB Math 53/54). We emphasize that this document is not a replacement for the prerequisite classes.

E[X] expected value of random variable X Var(X) variance of random variable X Cov(X;Y) covariance of random variables Xand Y Other notes: Vectors and matrices are in bold (e.g. x;A). This is true for vectors in Rn as well as for vectors in general vector spaces. We generally use Greek letters for scalars and capital Roman

Fullscreen Download
Loading..

Tags:

  Machine, Learning, Variance, Vector, Random, Machine learning, Covariance

Information

Domain:

Source:

Link to this page:

Please notify us if you found a problem with this document:

Spam in document Broken preview Other abuse

Transcription of Mathematics for Machine Learning - GitHub Pages

Documents from same domain

Mathematics for Machine Learning - GitHub Pages

gwthomas.github.io

Mathematics for Machine Learning Garrett Thomas Department of Electrical Engineering and Computer Sciences University of California, Berkeley January 11, 2018 1 About Machine learning uses tools from a variety of mathematical elds. This document is an attempt to provide a summary of the mathematical background needed for an introductory class ...

  Engineering, Machine, Mathematics, Learning, Machine learning

Related documents

3. The Multivariate Normal Distribution

www.math.hkbu.edu.hk

(a) nd the mean and variance of the linear combination a0X 1 of the three components of X 1 where a= [a 1 a 2 a 3]0. (b)Consider two linear combinations of random vectors 1 2 X 1 + 1 2 X 2 + 1 2 X 3 + 1 2 X 4 and X 1 + X 2 + X 3 3X 4: Find the mean vector and covariance matrix for each linear combination of vectors and also the covariance ...

  Distribution, Variance, Matrix, Normal, Vector, Multivariate, Random, Covariance, Covariance matrix, Random vectors, The multivariate normal distribution

Lecture 7 — Spectral methods 7.1 Linear algebra review

cseweb.ucsd.edu

Suppose random vector X ∈Rd has mean µ and covariance matrix M. Then zTMz represents the variance of X in direction z: var(zTX) = E[(zT(X −µ))2] = E[zT(X −µ)(X −µ)Tz] = zTMz. Theorem 7 tells us that the direction of maximum variance is u1, and that of minimum variance is ud. Continuing with this example, suppose that we are ...

  Variance, Matrix, Random, Covariance, Covariance matrix

Gaussian Random Vectors - Math

www.math.utah.edu

Gaussian Random Vectors 1. The multivariate normal distribution Let X:= (X1 ￿￿￿￿￿X￿)￿ be a random vector. We say that X is a Gaussian random vector if we can write X = µ +AZ￿ where µ ∈ R￿, A is an ￿ × ￿ matrix and Z:= (Z1 ￿￿￿￿￿Z￿)￿ is a ￿-vector of i.i.d. standard normal random variables. Proposition 1.

  Matrix, Vector, Random, Random vectors

Principal Components Analysis

www.stat.cmu.edu

we use q principal components, our weight matrix w will be a p ×q matrix, where each column will be a different eigenvector of the covariance matrix v. The eigen-values will give the total variance described by each component. The variance of the projections on to the first q principal components is then ￿ q i=1 λ i.

  Variance, Matrix, Covariance, Covariance matrix

Kalman Filtering Tutorial

biorobotics.ri.cmu.edu

and is a symmetric n by n matrix and is positive definite unless there is a linear dependence among the components of x. The (i,j) th element of P xx is sx x i j 2 Interpreting a covariance matrix: diagonal elements are the variances, off-diagonal encode correlations.

  Matrix, Covariance, Kalman, Covariance matrix

MATLAB Basic Functions Reference - MathWorks

www.mathworks.com

zeros(m,n) Create m x n matrix of zeros ones(m,n) Create m x n matrix of ones eye(n) Create a n x n identity matrix A=diag(x) Create diagonal matrix from vector x=diag(A) Get diagonal elements of matrix meshgrid(x,y) Create 2D and 3D grids rand(m,n), randi Create uniformly distributed random numbers or integers

  Basics, Reference, Functions, Matrix, Matlab, Random, Matlab basic functions reference

Negative Binomial Regression Models and Estimation …

www.icpsr.umich.edu

where, exp 0 i is defined as a random intercept; 0 1 exp K iijj j x is the log-link between the Poisson mean and the covariates or independent variables xs; and s are the regression coefficients. As discussed in Appendix C, the relationship can also be formulated using vectors, such that exp(x'β) i i.

  Model, Vector, Regression, Estimation, Negative, Random, Binomial, Negative binomial regression models and estimation

Related search queries

3. The Multivariate Normal Distribution, Variance, Random Vectors, Covariance matrix, Vectors, Covariance, Random, Matrix, Kalman, MATLAB Basic Functions Reference, Negative Binomial Regression Models and Estimation