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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 applications

Pattern Recognition Applications


Pattern recognition applications1

Pattern Recognition Applications


Pattern recognition applications2

Pattern Recognition Applications


Pattern recognition applications3

Pattern Recognition Applications


Pattern recognition applications4

Pattern Recognition Applications


Pattern recognition applications5

Pattern Recognition Applications


Pattern recognition applications6

Pattern Recognition Applications


Pattern recognition applications7

Pattern Recognition Applications


Pattern recognition applications8

Pattern Recognition Applications

Figure 9: Clustering of Microarray Data


Pattern recognition applications9

Pattern Recognition Applications

Figure 10: Brain Control Interface


Regression polynomial curve fitting

Regression: Polynomial Curve Fitting

is continuous


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:


Polynomial coefficients

Polynomial Coefficients


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


Regularization

Regularization:


Regularization1

Regularization:


Regularization vs

Regularization: vs.


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:


Pattern recognition approaches

Pattern recognition approaches


Statistical pattern recognition

Statistical Pattern recognition


Statistical pattern recognition1

Statistical Pattern recognition


Structural pattern recognition

Structural Pattern Recognition


Neural pattern recognition

Neural Pattern Recognition


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