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Csc446: Pattren Recognition (LN1)

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Lecture 1: Introduction

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Csc446: Pattren Recognition (LN1)

  1. 1. Slide - 1 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
  2. 2. Slide - 2 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  3. 3. Slide - 3 • 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  4. 4. Slide - 4 Prerequisites: – CSW150: Structured Programming. – SCC223 : Probability & Statistics – SCC332 : Numerical Methods – CSC343 : Artificial Intelligence CSC446 : Course Organization & Guidelines Refresh your Information ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  5. 5. Slide - 5 Grading: • Midterm Exam (10 points) • Assignments, Quizzes (10 points) • Final Project, Lab test (15 points) • Final Exam (65 points) CSC446 : Course Organization & Guidelines ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  6. 6. Slide - 6 CSC446 : Course Organization & Guidelines 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. ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  7. 7. Slide - 7 CSC446 : Course Organization & Guidelines 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 … ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  8. 8. Slide - 8 CSC446 : Course Organization & Guidelines 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  9. 9. Slide - 9 CSC446 : Course Organization & Guidelines 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/ ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  10. 10. Slide - 10 Chapter 1: Introduction to Pattern Recognition CSC446 : Pattern Recognition (Read all sections in Chapter 1) ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  11. 11. Slide - 11 • 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  12. 12. Slide - 12 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  13. 13. Slide - 13 Pattern Recognition Systems What is a Pattern? What is a Pattern Recognition System? ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  14. 14. Slide - 14 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. ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  15. 15. Slide - 15 Examples of Patterns ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  16. 16. Slide - 16 Examples of Patterns Speech Signal ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  17. 17. Slide - 17 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) ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  18. 18. Slide - 18 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. ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  19. 19. Slide - 19 Applications of PRS Applications: In Military A remote-controlled bomb disposal robot in action. Photo by courtesy of US Airforce . ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  20. 20. Slide - 20 Applications of PRS • OCR: Handwritten/printed optical characters recognition. ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  21. 21. Slide - 21 Applications of PRS –Dictation machines, Voice Command, HCI HCI, Archiving Dictation Voice Command ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  22. 22. Slide - 22 Applications of PRS •Investigation: Lie detector, ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  23. 23. Slide - 23 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  24. 24. Slide - 24 Machine Learning Concepts Readings: Chapter 1 in Bishop’s PRML ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  25. 25. Slide - 25 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. ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  26. 26. Slide - 26 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. ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  27. 27. Slide - 27 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  28. 28. Slide - 28 Supervised Learning Examples • Regression: Y continuous Debt/equity Earnings company stock price Future product orders ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  29. 29. Slide - 29 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 } ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  30. 30. Slide - 30 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 ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  31. 31. Slide - 31 Unsupervised Learning Examples • Clustering: Group “similar” examples together ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  32. 32. Slide - 32 Unsupervised Learning Examples • Density Estimation: Find probability density p(x) Model used : Mixture of Gaussians ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  33. 33. Slide - 33 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. ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq
  34. 34. Slide - 34 Next Time Introduction to Pattern Recognition ASU-CSC446 : Pattern Recognition. Prof. Dr. Mostafa Gadal-Haqq

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