Csc446: Pattren Recognition (LN1)

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CSC446 : Pattern Recognition
Prof. Dr. Mostafa G. M. Mostafa
Faculty of Computer & Information Sciences
Computer Science Department
AIN SHAMS UNIVERSITY
Lecture Note 1:
Course Organization &
Chapter 1: Introduction to PRS
ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq

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CSC446: Patt Recog - Course Outline
• Introduction to PRS
• Mathematical Foundations
• Supervised Learning
• Bayesian Decision Theory
• Maximum Likelihood Estimation
• Non-Parametric Methods
• Linear Discriminant Functions & NN
• Unsupervised Learning
• K-mean Clustering

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• Text Book:
– Duda, Hart, and Stork. “Pattern
Classification”, 2nd ed. Wiley, 2001.
• Reference Book:
– C. M. Bishop. “Pattern Recognition & Machine
Learning”. Springer, 2007.
– A. Webb. “Statistical Pattern Recognition”. Arnold,
1999.
• Lab book: Handout materials + “Matlab
Getting Started” and “Building GUI” tutorials.
CSC446 : Course Organization & Guidelines

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Prerequisites:
– CSW150: Structured Programming.
– SCC223 : Probability & Statistics
– SCC332 : Numerical Methods
– CSC343 : Artificial Intelligence
Refresh your Information

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Grading:
• Midterm Exam (10 points)
• Assignments, Quizzes (10 points)
• Final Project, Lab test (15 points)
• Final Exam (65 points)

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Lecture Protocol:
Feel free to interrupt and ask ME.
DON’T ask/talk to your colleagues.
Programming and homework assignments
•Late answers are given 50% of the mark.
 Slides are available in pdf format.

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How to pass this course?
– You will learn a lot during this course, but you
will have to work hard to pass it!
– Don’t accumulate …
– Do it yourself …
– Ask for help …

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Warning:
– Working policy: You are encouraged to collaborate
in study groups. But submitting a copy or slightly
changed others’ solutions or codes is Cheating.
– Cheating will be punished severely
• Assignments: All get 0
• Midterm or Final: you will get Fail

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Resources (Most important):
 Societies:
IAPR: http://www.iapr.org/
 Journals:
PAMI: http://www.computer.org/tpami/
PR: http://www.elsevier.com/locate/pr
PRL: http://www.elsevier.com/locate/prl
 Web Sites:
PRInfo: http://www.ph.tn.tudelft.nl/PRInfo/

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Chapter 1:
Introduction to Pattern
Recognition
CSC446 : Pattern Recognition
(Read all sections in Chapter 1)

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• Objectives of Pattern Recognition Systems
• Applications of Pattern Recognition Systems
• What is a Pattern Recognition System?
• An intuitive Example
• Components of Pattern Recognition Systems
• The Design Cycle
• Learning and Adaptation
– Supervised, Unsupervised, and Reinforcement Learning.
Intro Pattern Recognition - Outline

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PRS Objective:
• Building a machine that can learn and
recognize patterns as human,
• Having such a machine is immensely useful
to mankind.
Pattern Recognition System

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Pattern Recognition Systems
What is a Pattern?
What is a Pattern Recognition
System?

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What is a Pattern?
• A pattern is a set of instances which share
some regularities, and are similar to each
other in the set.
• A pattern should occur repeatedly.
• A pattern is observable, sometimes partially,
by some sensors with noise and distortions.

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Examples of Patterns

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Examples of Patterns
Speech Signal

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What is Pattern Recognition?
• Definition: “The act of taking in raw data
and taking an action based on the category
of the pattern found in the data.”
an object
Decision
raw data
Pattern
Recognition
System (Cylinder)

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Applications of PRS
Applications: Robotics
Photo by courtesy of US Department of Energy .
Robot Manny is developed
at Battelle's Pacific
Northwest Laboratories in
Richand, Washington. It
took 12 researchers 3
years (1986-1989) and $2
million to develop this
robot. Manny was built
for the U.S. Army in the
late 1980s to test protective
clothing.

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Applications of PRS
Applications: In Military
A remote-controlled
bomb disposal robot
in action.
Photo by courtesy of US Airforce .

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Applications of PRS
• OCR: Handwritten/printed optical characters recognition.

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Applications of PRS
–Dictation machines, Voice Command, HCI
HCI,
Archiving
Dictation
Voice
Command

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Applications of PRS
•Investigation: Lie detector,

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Applications of PRS
•Other Applications:
– Manufacturing:
• Defect detection in chip manufacturing
• Fruit/vegetable recognition
– Biometrics: voice, iris, fingerprint, face, gait
recognition
– Medical diagnosis
–Smell recognition (e-nose, sensor networks)
–Bioinformatics: classification of DNA sequences.
–Security: intrusion detection

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Machine Learning
Concepts
Readings: Chapter 1 in Bishop’s PRML

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Machine Learning
• Learning Process:
–Learner (a computer program; an agent) processes data D
representing past experiences and tries to either develop an
appropriate response to future data, or describe the seen
data in some meaningful way.
• Example:
– Learner sees a set of patient records with corresponding
diagnoses. It can either try to :
– predict the presence of a disease for future patients.
– describe the dependencies between diseases, symptoms.

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Types of Machine Learning
• Supervised learning
– Learning mapping between input x and desired output y
– Teacher presents some samples of pairs (x, y)
• Unsupervised learning
– Learning relations between data components
– No teacher signal.
• Reinforcement learning
– Learning mapping between input x and desired output y
– Teacher gives a Critic signal (reinforcement) of
how good the response was.

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Supervised Learning
• Data: A set of n examples
𝑿 = {𝒙 𝟏, 𝒙 𝟐, …, 𝒙 𝒏} & 𝑻 = {𝒕 𝟏, 𝒕 𝟐, …, 𝒕 𝒏}
x is input vector, and t is desired output (given by a teacher).
• Objective: Learn the mapping 𝑭: 𝑿 → 𝒀
That is to find: 𝒚𝒊 ≈ 𝒇(𝒙𝒊) for all 𝒊 = 𝟏, … , 𝒏
• Two types of problems:
Regression: X discrete or continuous Y is continuous
Classification: X discrete or continuous Y is discrete

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Supervised Learning Examples
• Regression: Y continuous
Debt/equity
Earnings company stock price
Future product orders

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Supervised Learning Examples
• Classification: Y discrete
{ a, b, c, …, x, y, z}
X is a vector/sequence of values
{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }

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Unsupervised Learning
• Data: A set of n examples
𝑿 = {𝒙 𝟏, 𝒙 𝟐, …, 𝒙 𝒏}
desired output NOT GIVEN (no teacher).
• Objective:
– Learn the relation between data components
• Two types of problems:
Clustering: Group “similar” examples together
Density Estimation : Model probabilistically the samples

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Unsupervised Learning Examples
• Clustering: Group “similar” examples together

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Unsupervised Learning Examples
• Density Estimation: Find probability density p(x)
Model used : Mixture of Gaussians

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Reinforcement Learning
• We want to learn: 𝑭: 𝑿 → 𝒀
• We only see samples of x but not y
• Instead of getting y we get a feedback
(reinforcement) from a critic about how good our
output was.
• Example:
– Real time strategic (RTS) games.

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Next Time
Introduction to Pattern
Recognition

Csc446: Pattren Recognition (LN1)

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