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### Human Perception

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

Evaluation

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

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

Over-fitting

Root-Mean-Square (RMS) Error:

Data Set Size:

9th Order Polynomial

Data Set Size:

9th Order Polynomial

Regularization ;ridge regression

Penalize large coefficient values

Shrinkage: reduce the order of method

Polynomial Coefficients

Optimization Problem: Finding optimum

Classification example: Handwritten Digit Recognition

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

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