|
課程名稱 |
圖形分析辨認
PATTERN ANALYSIS AND CLASSIFICATION |
|
開課學期 |
96-2 |
|
授課對象 |
電機資訊學院 資訊網路與多媒體研究所 |
|
授課教師 |
洪一平 |
|
課號 |
CSIE5079 |
|
課程識別碼 |
922 U3030 |
|
班次 |
|
|
學分 |
3 |
|
全/半年 |
半年 |
|
必/選修 |
選修 |
|
上課時間 |
星期三7,8,9(14:20~17:20) |
|
上課地點 |
資102 |
|
備註 |
總人數上限：98人 |
|
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/962PR |
|
課程簡介影片 |
|
|
核心能力關聯 |
核心能力與課程規劃關聯圖 |
|
課程大綱
|
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
|
課程概述 |
The outline of this course is given below.
I. Pattern Recognition Overview
II. Bayesian Decision Theory
III. Supervised Learning Using Parametric Approaches
IV. Supervised Learning Using Non-parametric Approaches
V. Linear Discriminant Functions
VI. Unsupervised Learning and Clustering
VII. Special Topics in PR
|
|
課程目標 |
The 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, image analysis, video surveillance,
face recognition, fingerprint identification, speech recognition,
medical diagnosis, data mining, and information retrieval. |
|
課程要求 |
|
|
預期每週課後學習時數 |
|
|
Office Hours |
|
|
指定閱讀 |
|
|
參考書目 |
Textbook：
R. Duda, P. Hart, D. Stork, `Pattern Classification and Scene
Analysis,` second edition, John Wiley and Sons, 2001.
Reference Book:
S. Theodoridis, K. Koutroumbas, Pattern Recognition, 3rd ed.,
Academic Press, 2006.
|
|
評量方式
(僅供參考) |
|
No. |
項目 |
百分比 |
說明 |
|
1. |
Homeworks |
30% |
|
2. |
Two Exams |
40% |
|
3. |
Term Project |
30% |
|
|
|
週次 |
日期 |
單元主題 |
|
第1週 |
02/20 |
Introduction // Chapter 1. Overview |
|
第2週 |
02/27 |
Chapter 1. Overview //
Chapter 2. Bayesian Decision Theory |
|
第3週 |
03/05 |
Chapter 2 Baysian Decision Theory |
|
第4週 |
03/12 |
Chapter 2 Baysian Decision Theory |
|
第5週 |
03/19 |
Chapter 2 Baysian Decision Theory /
Chapter 3 Supervised Learning Using Parametric Approaches (MLE) |
|
第6週 |
03/26 |
First Exam |
|
第7週 |
0402 |
Chapter 3 Supervised Learning Using Parametric Approaches (Bayesian Est) |
|
第8週 |
0409 |
Chapter 3 Supervised Learning Using Parametric Approaches (PCA) |
|
第9週 |
04/16 |
Chapter 3 Supervised Learning Using Parametric Approaches (LDA) |
|
第10週 |
04/23 |
Chapter 4 Supervised Learning Using Nonparametric Approaches |
|
第11週 |
04/30 |
Chapter 10 Unsupervised Learning and Clustering |
|
第12週 |
05/07 |
2ND EXAM |
|
第13週 |
05/14 |
Term Project Description + Background Modeling + Cascaded Adaboost + People Detection |
|
第14週 |
05/21 |
Vehicle Detection + Manifold Learning |
|
第15週 |
05/28 |
Chapter 5 Linear Discriminant Functions + 多面向自我覺察 |
|
第16週 |
06/04 |
Chapter 5 Linear Discriminant Functions + Fuzzy Clustering |
|
第17週 |
06/11 |
SVM ＋ Chapter 10 Unsupervised Learning and Clustering |
|
第18週 |
06/18 |
Demo of Term Projects |
|