'Linear transformations' diaporamas de présentation

Engineering Analysis

Engineering Analysis. Chapter 4 Linear Transformations. Linear mappings from one vector space to another play an important role in mathematics. This chapter provides an introduction to the theory of such mappings. 4.1 Definition and Examples.

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Warping

Warping. CSE 590 Computational Photography Tamara Berg. Picture Time!. Image Warping. http://www.jeffrey-martin.com. Slides from: Alexei Efros & Steve Seitz. Image Warping in Biology . D'Arcy Thompson http://www-groups.dcs.st-and.ac.uk/~history/Miscellaneous/darcy.html

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A Big Data Spreadsheet

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More on the linear model

More on the linear model. Categorical predictors. men. RT ~ Noise + Gender. women. resp ~ Condition. Demo. set.seed (666) pred = c(rep(0,20),rep(1,20)) resp = c( rnorm (20,mean=2,sd=1), rnorm (20,mean=2,sd=1)) for( i in 1:10){ resp = c( resp [1:20], resp [21:40]+1)

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Computer Graphics Image warping and morphing

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Vectors & Matrices

Vectors & Matrices. CSE167: Computer Graphics Instructor: Steve Rotenberg UCSD, Fall 2006. Project 1. Make a program that renders a simple 3D object (like a cube). It should render several copies of the same object with different positions/rotations.

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Introduction to Computer Graphics CS 445 / 645

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Image Warping

Image Warping. http://www.jeffrey-martin.com. 15-463: Computational Photography Alexei Efros, CMU, Fall 2012. Some slides from Steve Seitz. f. f. f. T. T. x. x. x. f. x. Image Transformations. image filtering: change range of image g(x) = T(f(x)).

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Image Warping

Image Warping. http://www.jeffrey-martin.com. 15-463: Computational Photography Alexei Efros, CMU, Fall 2008. Some slides from Steve Seitz. f. f. f. T. T. x. x. x. f. x. Image Transformations. image filtering: change range of image g(x) = T(f(x)).

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Graham Hesketh Information Engineering Group Rolls-Royce Strategic Research Centre

Kalman Filters for Fun. Graham Hesketh Information Engineering Group Rolls-Royce Strategic Research Centre. Basic Kalman Filter Equations. Prediction. Updating (correction). Definitions. System state: mean estimate. x - vector of size q e.g. for a single channel,

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Transformations & Data Cleaning

Transformations & Data Cleaning. Linear & non-linear transformations 2-kinds of Z-scores Identifying Outliers & Influential Cases Univariate Outlier Analyses -- trimming vs. Winsorizing Outlier Analyses for r & ANOVA Outlier Analyses for Multiple Regression. Transformations

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IR Models: Latent Semantic Analysis

IR Models: Latent Semantic Analysis. Algebraic. Set Theoretic. Generalized Vector Lat. Semantic Index Neural Networks. Structured Models. Fuzzy Extended Boolean. Non-Overlapping Lists Proximal Nodes. Classic Models. Probabilistic. boolean vector probabilistic.

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Writing Student-Centered Learning Goals/Outcomes

Writing Student-Centered Learning Goals/Outcomes. A tutorial created by the Siena Assessment Planning Committee. What should learning outcomes accomplish ?. Articulating learning outcomes will help:

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Chapter 6: Eigenvalue Problems

Chapter 6: Eigenvalue Problems. Motivation Differential Equations Other Equations. 6.1 Motivation. 6.1 Motivation. Physical Systems are commonly described by ( usually differential) equations involving sets of coupled variables.

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Section 4.2

Section 4.2. Linear Transformations from R n to R m. DOMAIN, CODOMAIN, AND RANGE OF A FUNCTION. Let f be a function from the set A into the set B . The set A is called the domain of f . The set B is called the codomain of f .

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Measure Types

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Transformations

Transformations. Process. Make scatterplot of ( x,y ) Check for linearity Look at residual plot (x, resid ) Check for random scattering of points Look at r 2 Higher r 2 ? Better model Begin transformations. Linear Transformations. LinReg (log x, y) LinReg (x, log y)

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