The book contains a large number of examples, interspersed throughout the text, with a case study and worked numerical lab at the end of every chapter:

Case
Study
2:
Biometrics

Case
Study
3:
The
Netflix
Prize

Case
Study
4:
Defect
Detection

Case
Study
5:
Image
Searching

Case
Study
6:
Hand-Writing
Recognition

Case
Study
7:
Object
Recognition

Case
Study
8:
Medical
Assessments

Case
Study
9:
Autonomous
Vehicles

Case
Study
10:
Digital
Communications

Case
Study
11:
Interpretability
and
Ethics
of
Large
Networks

Case
Study
12:
Ancient
Text
Analysis:
Who
Wrote
What?

Example
2.1:
Pattern
Recognition
of
Text

Example
2.2:
Pattern
Recognition
of
the
Mind

Example
2.3:
What
is
a
Class

Example
2.4:
The
Types
of
Pattern
Recognition

Example
3.1:
Robustness
in
Learning

Example
3.2:
Regression
and
Classification

Example
3.3:
An
overview
of
the
use
of
data
in
learning

Example
3.4:
Wrapper-Based
Learning

Example
4.1:
Face
Recognition

Example
4.2:
How
to
Form
an
Ellipsoid

Example
4.3:
Quadratic
Forms

Example
4.4:
Ellipsoids
and
Real
Data

Example
4.5:
Class
Data,
Covariances,
Eigendecompositions,
and
Ellipses

Example 6.1: Euclidean Distance and Measurement Units

Example
7.1:
Learning
of
Mixture
Models

Example
7.2:
Nonlinear
Maximum
Likelihood

Example
8.1:
Are
we
Classifying
or
Estimating

Example
8.2:
Classifier
Probability
of
Error

Example
8.3:
Classifier
Probability
of
Error
for
Gaussian
Statistics

Example
9.1:
Data
Augmentation

Example
9.2:
Receiver
Operating
Characteristics

Example
10.1:
Mean-Squared
Error
Discriminant

Example
10.2:
Radial
Basis
Functions

Example
11.1:
Bootstrap
and
Estimation

Example
11.2:
Biological
and
Artificial
Neurons

Example
11.3:
Illusions
in
Machine
Learninge

Example
12.1:
Vector
Quantization

Example
12.2:
Bag-of-Words
and
Visual
Representation

Example B.1: Power Laws and Infinite Variance

Lab
2:
The
Iris
Dataset

Lab
3:
Overfitting
and
Underfitting

Lab
4:
Working
with
Random
Numbers

Lab
5:
Extracting
Features
and
Plotting
Classes

Lab
6:
Distance-Based
Classifiers

Lab
7:
Parametric
and
Nonparametric
Estimation

Lab
8:
Statistical
and
Distance-Based
Classifiers

Lab
9:
Leave-One-Out
Validation

Lab
10:
Discriminants

Lab
11:
Ensemble
Classifiers

Lab
12:
Clustering