EE 546
Pattern Recognition
Lecture # 1
Course webpage:
https:/cms.iyte.edu.tr/course/view.php?id=378
(No Enrollment Key!)
Instructor: M. Zbeyir nl, PhD
zubeyirunlu@iyte.edu.tr
September 24, 2014
Lecture # 1
1
Introduction
Outline of todays course
What is
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Lecture # 12
Unsupervised Learning
Nonparametric U
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Clustering
Lecture # 12
1
Today
New Topic: Unsupervised Learning
Supervised vs. unsupervised learning
Unsupervised learning
N
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Lecture # 11
Multilayer Neural Networks
Lecture # 11
1
Today
Multilayer Neural Networks
Inspiration from Biology
History
Perceptron
Multilayer perceptron
Brain vs. Computer
Designed to solve logic and
arithmetic p
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Pattern Recognition
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Lecture # 10
Support Vector Machines
Lecture # 10
1
Today
Support Vector Machines (SVM)
Introduction
Linear Discriminant
Linearly Separable Case
Linearly Non Separable Case
Kernel Trick
Non Linear Discriminant
SVM
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Pattern Recognition
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Lecture # 9
Linear Discriminant Functions
Lecture # 9
1
Today
Continue with Linear Discriminant Functions
Last lecture: Perceptron Rule for weight learning
This lecture: Minimum Squared Error (MSE) rule
Pseudoinve
EE 546
Pattern Recognition
Lecture # 7
Nonparametric Techniques
(Continue)
October 29, 2014
Lecture # 7
1
Today
Introduction to nonparametric techniques
Basic Issues in Density Estimation
Two Density Estimation Methods
1. Parzen Windows
2. Nearest Neighbo
EE 546
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Pattern Recognition
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Lecture # 13
Unsupervised Learning
Parametric Unsupervised Learning
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Expectation Maximization
Lecture # 13
1
Today
Parametric Unsupervised Learning
Expectation Maximization (EM)
one of the most usef
Homework # 2
EE546 Pattern Recognition 2014 Fall IYTE
Deadline: October 15, 2014
You may use Matlab anytime if you need!
1 and 2 are two classes, and x is a one-dimensional feature. The likelihood functions
for these two classess are given as follows:
p(
EE 546
Pattern Recognition
Lecture # 6
Nonparametric Techniques
October 29, 2014
Lecture # 6
1
Today
Introduction to nonparametric techniques
Basic Issues in Density Estimation
Two Density Estimation Methods
1. Parzen Windows
2. Nearest Neighbors
October
EE 546
Pattern Recognition
Lecture # 5
Parameter Estimation
1. Maximum Likelihood
2. Bayesian
October 22, 2014
Lecture # 5
1
Introduction
October 22, 2014
Lecture # 5
2
Introduction
Bayesian Decision Theory in previous lectures tells us
how to design an o
Electrical & Electronics Engineering Department
EE546 Pattern Recognition Homework # 3
Deadline: December 19, 2014, 14:30
Important Note: Please include all the codes you wrote. Dont forget adding your comments about the method and the
results for each qu
EE 546
Pattern Recognition
Lecture # 4
The Normal Density
October 15, 2014
Lecture # 4
1
The Normal (Gaussian) Density
The structure of a Bayes Classifier is determined by
the conditional densities p(x |wi) as well as by the
priori probabilities
The Norma
EE 546
Pattern Recognition
Lecture # 3
Bayesian Decision Theory
October 8, 2014
Lecture # 3
1
Bayesian Decision Theory
Bayesian Decision Theory is a fundamental
statistical approach that quantifies the tradeoffs
between various decisions using probabiliti
EE 546
Pattern Recognition
Lecture # 2
Dimensionality Reduction:
Feature Extraction & Selection
October 1, 2014
Lecture # 2
1
Data Acquisition
input
One of the most important
requirements for designing a
successful pattern recognition
system is to have ad
Problem Set 2
MAS 622J/1.126J: Pattern Recognition and Analysis
Due: 5:00 p.m. on September 30
[Note: All instructions to plot data or write a program should be carried
out using Matlab. In order to maintain a reasonable level of consistency and
simplicit