INFORMATION RETRIEVAL LINEAR ALGEBRA REVIEW Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.

Slides:



Advertisements
Similar presentations
Section 4.1 – Vectors (in component form)
Advertisements

Vector Operations in R 3 Section 6.7. Standard Unit Vectors in R 3 The standard unit vectors, i(1,0,0), j(0,1,0) and k(0,0,1) can be used to form any.
$100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 6- 1.
Vector Products (Dot Product). Vector Algebra The Three Products.
13.1 – Solving Right Triangles
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm Shang-Hua Teng.
CSC482 INTRODUCTION TO TEXT ANALYTICS COURSE INTRODUCTION: PART ONE Thomas Tiahrt, MA, PhD.
PROBABILITY REVIEW PART 9 CONDITIONAL PROBABILITY II Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
MapR – HADOOP DEVELOPMENT IN A VIRTUAL MACHINE Thomas Tiahrt, MA, PhD CSC482 Introduction to Text Analytics.
INFORMATION THEORY BAYESIAN STATISTICS I Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION THEORY BAYESIAN STATISTICS II Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
PROBABILITY REVIEW PART 4 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
PROBABILITY REVIEW PART 5 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INTRODUCTION TO PYTHON PART 2 INPUT AND OUTPUT CSC482 Introduction to Text Analytics Thomas Tiahrt, MA, PhD.
TEXT CATEGORIZATION THE FEDERALIST - PART 2 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
TEXT CATEGORIZATION THE FEDERALIST – PART 1 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 3 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 2 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
COURSE OVERVIEW ADVANCED TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
TEXT CATEGORIZATION THE FEDERALIST – PART 3 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 1 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION RETRIEVAL VECTOR SPACE MODEL IN-DEPTH PART 5 Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION THEORY CONDITIONAL ENTROPY Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
INFORMATION THEORY SIMPLIFIED POLYNESIAN LANGUAGE EXAMPLE Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
PROBABILITY REVIEW PART 2 PROBABILITY FOR TEXT ANALYTICS Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
$100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300.
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley 11.9 Curvature and Normal Vectors.
GOOGLE N-GRAMS ON AMAZON WEB SERVICES PART 2 Thomas Tiahrt, MA, PhD Computer Science 482 – Introduction to Text Analytics.
VECTORS (Ch. 12) Vectors in the plane Definition: A vector v in the Cartesian plane is an ordered pair of real numbers:  a,b . We write v =  a,b  and.
CGDD 4003 THE MATH LECTURE (BOILED DOWN, YET LIGHTLY SALTED)
INFORMATION THEORY POLYNESIAN REVISITED Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
Linear Algebra Diyako Ghaderyan 1 Contents:  Linear Equations in Linear Algebra  Matrix Algebra  Determinants  Vector Spaces  Eigenvalues.
Linear Algebra Diyako Ghaderyan 1 Contents:  Linear Equations in Linear Algebra  Matrix Algebra  Determinants  Vector Spaces  Eigenvalues.
EE 460 Advanced Control and System Integration
Ranked Retrieval INST 734 Module 3 Doug Oard. Agenda Ranked retrieval  Similarity-based ranking Probability-based ranking.
1.7 Linear Independence. in R n is said to be linearly independent if has only the trivial solution. in R n is said to be linearly dependent if there.
MAT 2401 Linear Algebra 4.4 II Spanning Sets and Linear Independence
is a linear combination of and depends upon and is called a DEPENDENT set.
Kansas State University Department of Computing and Information Sciences CIS 736: Computer Graphics Wednesday, 12 April 2006 William H. Hsu Department.
Linear Algebra Chapter 4 n Linear Algebra with Applications –-Gareth Williams n Br. Joel Baumeyer, F.S.C.
Chapter 4 Vector Spaces Linear Algebra. Ch04_2 Definition 1: ……………………………………………………………………. The elements in R n called …………. 4.1 The vector Space R n Addition.
MAT 2401 Linear Algebra 4.5 Basis and Dimension
Algebra Review. Systems of Equations Review: Substitution Linear Combination 2 Methods to Solve:
Kansas State University Department of Computing and Information Sciences CIS 736: Computer Graphics Monday, 17 April 2006 William H. Hsu Department of.
Slides for CISC 2315: Discrete Structures Chapters CISC 2315 Discrete Structures Professor William G. Tanner, Jr. Fall 2007 Slides created by James.
Section 6.3 Vectors 1. The student will represent vectors as directed line segments and write them in component form 2. The student will perform basic.
SECTION 3.2 SOLVING LINEAR SYSTEMS ALGEBRAICALLY Advanced Algebra Notes.
Background for Machine Learning (I) Usman Roshan.
Notes Over 1.2.
Does the set S SPAN R3 ?.
Math II: Unit 1: Transformations
continued on next slide
Advanced Engineering Mathematics, Third Edition
                                                                                                                                                                                                                                                
continued on next slide
continued on next slide
Vectors, Linear Combinations and Linear Independence
Anton/Busby Contemporary Linear Algebra
Scalars Some quantities, like temperature, distance, height, area, and volume, can be represented by a ________________ that indicates __________________,
Notes: 8-1 Geometric Vectors
Section 3.1 – Vectors in Component Form
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm
Vector Spaces, Subspaces
1) Write the vector in component form.
Transformations.
Notes: 8-1 Geometric Vectors
continued on next slide
Section 6.3 Vectors in the Plane.
continued on next slide
Presentation transcript:

INFORMATION RETRIEVAL LINEAR ALGEBRA REVIEW Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics

Vector Length 2

Angle between Vectors 3

Distance between Points 4

5

Linear Combinations 6

7

Linear Independence 8  Vector collections are either one of: linearly dependent linearly independent

Linear Independence 9

References 10 Sources: Linear Algebra by Gareth Williams, Jones and Bartlett Publishers.

The end of the second Linear Algebra review slide show has come. End of the Slides 11