Introduction Mohammad Beigi Department of Biomedical Engineering Isfahan University

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Presentation transcript:

Introduction Mohammad Beigi Department of Biomedical Engineering Isfahan University

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 Learning Texts  R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd edition, John Wiley & Sons, Inc., 2000.Pattern Classification  M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.Pattern Recognition and Machine Learning

Midterm 25% Final 40% Computer assignments 10% Final Programming Project 15% Seminar 10% Evaluation

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?  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  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

Figure 9: Clustering of Microarray Data

Pattern Recognition Applications Figure 10: Brain Control Interface

Regression: Polynomial Curve Fitting is continuous

Sum-of-Squares Error Function Optimization Problem

0 th Order Polynomial

1 st Order Polynomial

3 rd Order Polynomial

9 th Order Polynomial

Over-fitting Root-Mean-Square (RMS) Error:

Polynomial Coefficients

Data Set Size: 9 th Order Polynomial

Data Set Size: 9 th Order Polynomial

Regularization ;ridge regression Penalize large coefficient values Shrinkage: reduce the order of method

Regularization:

Regularization: vs.

Polynomial Coefficients Optimization Problem: Finding optimum

Classification example: Handwritten Digit Recognition 28*28 Pixel image  : 784 real numbers, training set:

Pattern recognition approaches

Statistical Pattern recognition

Structural Pattern Recognition

Neural Pattern Recognition