- 72 Views
- Uploaded on
- Presentation posted in: General

Introduction

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Mohammad Beigi

Department of Biomedical Engineering

Isfahan University

Majid.beigi@eng.ui.ac.ir

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 ?

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.

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

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

- 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.

- 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.

Figure 9: Clustering of Microarray Data

Figure 10: Brain Control Interface

is continuous

Optimization Problem

Root-Mean-Square (RMS) Error:

9th Order Polynomial

9th Order Polynomial

Penalize large coefficient values

Shrinkage: reduce the order of method

Optimization Problem: Finding optimum

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