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Introduction. Mohammad Beigi Department of Biomedical Engineering Isfahan University [email protected] Pattern recognition and Machine Learning. Syllabus Introduction, Linear Models for classification Neural Networks (MLP, RBF, SOM, LVQ, ADALINE)

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Introduction
Introduction

Mohammad Beigi

Department of Biomedical Engineering

Isfahan University

[email protected]


Pattern recognition and machine learning
Pattern recognition and Machine Learning

Syllabus

  • Introduction,

  • Linear Models for classification

  • Neural Networks (MLP, RBF, SOM, LVQ, ADALINE)

  • Kernel Methods & Support Vector Machines

  • Statistical Pattern Recognition ? (HMM,EM,

  • Clustering and unsupervised learning ?

  • Feature Selection and Dimension reduction ?


Pattern recognition and machine learning1
Pattern recognition and Machine Learning

Texts

  • R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2000.

  • M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.


Evaluation
Evaluation

  • Midterm 25%

  • Final 40%

  • Computer assignments 10%

  • Final Programming Project 15%

  • Seminar 10%


Human perception

Human Perception

Humans have developed highly sophisticated skills for sensing their environment and taking actions according to what they observe, e.g

Understanding spoken words

reading handwriting

distinguishing fresh food from its smell

We would like to give similar capabilities to machines


What is pattern recognition
What is Pattern Recognition?

  • A pattern is an entity, vaguely defined, that could be given a

    name, e.g.,

    • fingerprint image,

    • handwritten word,

    • human face,

    • speech signal,

    • DNA sequence,

  • Pattern recognition is the study of how machines can

    • observe the environment,

    • learn to distinguish patterns of interest,

    • make sound and reasonable decisions about the categories of the patterns.


Human and machine perception
Human and Machine Perception

  • We are often influenced by the knowledge of how patterns

    are modeled and recognized in nature when we develop

    pattern recognition algorithms.

  • Research on machine perception also helps us gain deeper

    understanding and appreciation for pattern recognition

    systems in nature.

  • Yet, we also apply many techniques that are purely

    numerical and do not have any correspondence in natural

    systems.










Pattern recognition applications8
Pattern Recognition Applications

Figure 9: Clustering of Microarray Data


Pattern recognition applications9
Pattern Recognition Applications

Figure 10: Brain Control Interface



Sum of squares error function
Sum-of-Squares Error Function

Optimization Problem


0 th order polynomial
0th Order Polynomial


1 st order polynomial
1st Order Polynomial


3 rd order polynomial
3rd Order Polynomial


9 th order polynomial
9th Order Polynomial


Over fitting
Over-fitting

Root-Mean-Square (RMS) Error:



Data set size
Data Set Size:

9th Order Polynomial


Data set size1
Data Set Size:

9th Order Polynomial


Regularization ridge regression
Regularization ;ridge regression

Penalize large coefficient values

Shrinkage: reduce the order of method





Polynomial coefficients1
Polynomial Coefficients

Optimization Problem: Finding optimum


Classification example handwritten digit recognition
Classification example: Handwritten Digit Recognition

28*28 Pixel image  : 784 real numbers, training set:







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