# Pattern Recognition and PowerPoint Presentation

2018-09-21 5K 5 0 0

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Machine Learning. Chapter 1: Introduction. Example. Handwritten Digit Recognition. Polynomial Curve Fitting . Sum-of-Squares Error Function. 0. th. Order Polynomial. 1. st. Order Polynomial. 3. rd. ID: 674441

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### Presentations text content in Pattern Recognition and

Slide1

Pattern Recognition

and Machine Learning

Chapter 1: Introduction

Slide2

Example

Handwritten Digit Recognition

Slide3
Polynomial Curve Fitting

Slide4
Sum-of-Squares Error Function

Slide5

0th Order Polynomial

Slide6

1st Order Polynomial

Slide7

3rd Order Polynomial

Slide8

9th Order Polynomial

Slide9
Over-fitting

Root-Mean-Square (RMS) Error:

Slide10
Polynomial Coefficients

Slide11
Data Set Size:

9

th

Order Polynomial

Slide12
Data Set Size:

9

th

Order Polynomial

Slide13
Regularization

Penalize large coefficient values

Slide14
Regularization:

Slide15
Regularization:

Slide16
Regularization: vs.

Slide17
Polynomial Coefficients

Slide18
Probability Theory

Apples

and

Oranges

Slide19

Probability Theory

Marginal Probability

Conditional Probability

Joint Probability

Slide20

Probability Theory

Sum Rule

Product Rule

Slide21
The Rules of Probability

Sum Rule

Product Rule

Slide22

Bayes’ Theorem

posterior

 likelihood × prior

Slide23

Probability Densities

Slide24
Transformed Densities

Slide25

Expectations

Conditional Expectation

(discrete)

Approximate Expectation

(discrete and continuous)

Slide26
Variances and

Covariances

Slide27
The Gaussian Distribution

Slide28
Gaussian Mean and Variance

Slide29
The Multivariate Gaussian

Slide30
Gaussian Parameter Estimation

Likelihood function

Slide31
Maximum (Log) Likelihood

Slide32

Properties of and

Slide33
Curve Fitting Re-visited

Slide34
Maximum Likelihood

Determine by minimizing sum-of-squares error, .

Slide35
Predictive Distribution

Slide36
MAP: A Step towards

Bayes

Determine by minimizing regularized sum-of-squares error, .

Slide37
Bayesian Curve Fitting

Slide38
Bayesian Predictive Distribution

Slide39
Model Selection

Cross-Validation

Slide40
Curse of Dimensionality

Slide41

Curse of Dimensionality

Polynomial curve fitting,

M

= 3

Gaussian Densities in

higher dimensions

Slide42
Decision Theory

Inference step

Determine either or .Decision step For given x

, determine optimal

t

.

Slide43
Minimum Misclassification Rate

Slide44
Minimum Expected Loss

Example: classify medical images as ‘cancer’ or ‘normal’

Decision

Truth

Slide45
Minimum Expected Loss

Regions are chosen to minimize

Slide46
Reject Option

Slide47
Why Separate Inference and Decision?

Minimizing risk (loss matrix may change over time)

Reject optionUnbalanced class priors

Combining models

Slide48
Decision Theory for Regression

Inference step

Determine .Decision step For given x

, make optimal

prediction,

y

(

x

)

,

for

t

.

Loss function:

Slide49
The Squared Loss Function

Slide50
Generative

vs Discriminative

Generative approach: Model Use Bayes’ theorem

Discriminative approach:

Model directly

Slide51
Entropy

Important quantity in

coding theory

statistical physics

machine learning

Slide52
Entropy

Coding theory:

x discrete with 8 possible states; how many bits to transmit the state of x?

All states equally likely

Slide53
Entropy

Slide54
Entropy

In how many ways can

N identical objects be allocated M bins?

Entropy maximized when

Slide55
Entropy

Slide56
Differential Entropy

Put bins of width

¢ along the real line

Differential entropy maximized (for fixed ) when

in which case

Slide57
Conditional Entropy

Slide58
The

Kullback-Leibler Divergence

Slide59
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