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CI1423 - Pattern Recognition

Second Semester 2005/2006

References

1. Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification , Second Edition, John Wiley & Sons, Inc., USA, 2001.
2. Tom M. Mitchell, Machine Learning , International Edition, McGraw-Hill, Singapore, 1997.
3. Stuart J. Russell and Peter Norvig, Artificial Intelligence – A Modern Approach , Second Edition, Prentice Hall – Pearson Education, Inc., New Jersey, USA, 2003.
4. Simon Haykin, Neural Networks: A Comprehensive Foundation , Second Edition, Prentice Hall, USA, 1999.
5. Robert J. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches , John Wiley & Sons, Inc., USA, 1992.

Slides

1. Introduction
2. Sketsa Wajah Anda
3. Duda R.O., Hart, P.E., Stork, D.G., Chapter 1: Introduction
4. Duda R.O., Hart, P.E., Stork, D.G., Chapter 2 Part 1: Bayesian Decision Theory (Sections 2.1-2.2)
5. Duda R.O., Hart, P.E., Stork, D.G., Chapter 2 Part 2: Bayesian Decision Theory (Sections 2.3-2.5)
6. Duda R.O., Hart, P.E., Stork, D.G., Chapter 2 Part 3: Bayesian Decision Theory (Sections 2.6,2-9)
7. Duda R.O., Hart, P.E., Stork, D.G., Chapter 3 Part 1: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)
8. Duda R.O., Hart, P.E., Stork, D.G., Chapter 3 Part 2: Maximum-Likelihood & Bayesian Parameter Estimation (part 2)
9. Duda R.O., Hart, P.E., Stork, D.G., Chapter 3 Part 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 3)
10. Duda R.O., Hart, P.E., Stork, D.G., Chapter 4 Part 1: Non-Parametric Classification (Sections 4.1-4.3)
11. Duda R.O., Hart, P.E., Stork, D.G., Chapter 4 Part 2: Non-Parametric Classification (Sections 4.3-4.5)
12. Duda R.O., Hart, P.E., Stork, D.G., Chapter 5: Linear Discriminant Functions (Sections 5.1-5-3)
13. Duda R.O., Hart, P.E., Stork, D.G., Chapter 6: Multilayer Neural Networks (Sections 6.1-6.3)
14. Duda R.O., Hart, P.E., Stork, D.G., Chapter 7: Stochastic Methods (Sections 7.-)
15. Duda R.O., Hart, P.E., Stork, D.G., Chapter 8: Nonmetric Methods (Sections 8.-)
16. Duda R.O., Hart, P.E., Stork, D.G., Chapter 9: Algorithm-Independent Machine Learning (Sections 9.-)

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Course overview

• Credit
  • 3 Credits (50 minutes x 3 = 150 minutes)

• Prerequisites
  • Linear Algebra (CI1402)
  • Computational Statistics 2 (CI1409)

• Goals
  • Student are able to decide which the proper machine learning method base on the problem representation
  • Student understand the usage of previous lectures (i.e., Linear Algebra, Design and Analysis Algorithm, Artificial Intelligence) for the problem of machine learning through pattern recognition
  • Student are able to know several techniques introducing in pattern recognition and able to apply it for real problems

• Contents
  • Introduction
  • Bayesian Decision Theory
  • Maximum-Likelihood & Bayesian Parameter Estimation
  • Non-Parametric Classification
  • Linear Discriminant Functions
  • Multilayer Neural Networks
  • Stochastic Methods
  • Nonmetric Methods
  • Algorithm-Independent Machine Learning

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