# 銘傳大學電腦與通訊工程學系課程大綱英文版

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17 Νοε 2013 (πριν από 4 χρόνια και 5 μήνες)

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Department of
Information
and Telecommunications
Engineering
,

Ming Chuan University

Course
Syllabus

Course
Name

Pattern Recognition

Course Number

05436

Total C
redits

3

R
equired / Elective

Elective

Course

Weekly
H
ours

3

Prereq
uisite

Course

Calculus,
Linear Algebra, Probability and Statistics

Course

Description

T
he goal of this course is to introduce the basic concepts and techniques used
in the field of pattern recognition (PR). Broadly speaking, PR is a science
that concerns
the classification (or recognition) of measurements. It has
many important applications, for example, document analysis, face
recognition, fingerprint identification, speech recognition, medical diagnosis,
data mining, and information retrieval.

Learning

O
bjectives

After completing this course, the student will
be
able to:

1.

Understand what are the different steps that enable a Pattern Recognition
(PR) system to classify an input pattern.

2.

Familiarize with the PR terminology such as: segmentation, feature
ex
traction, classification, post processing, training,
supervised/unsupervised learning and reinforcement learning.

3.

Measure or compute the minimum error rate classification and decision
regions for classical density functions.

Learning

Outcome

Upon succes
sful completion of this course, the student will have
demonstrated the abilities to:

1.

Compute the Maximum Likelihood (ML) estimator of some density
functions, distinguish between ML and Bayes methods.

2.
Develop code in MATLAB for nonparametric techniques
such as the
k
-
Nearest Neighbor rule to classify patterns.

3.
Apply linear discriminant function as a model of classification.

4.
Cluster data using k
-
means algorithm.

5.
Understand the Back
-
propagation algorithm central to the Neural Network
approach.

Course

Outline

This course includes the following major topics:

1
. Pattern Recognition Overview

2
. Bayesian Decision Theory

3
. Supervised Learning Using Parametric Approaches

4
. PCA and LDA

5.

Supervised Learning Using Non
-
parametric Approaches

6
. Line
ar Discriminant Functions

7
. Unsupervised Learning and Clustering

8
. Other Related Topics

Note

The Relationship between Learning Objectives and Department Goal

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