Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2020 280215 Pattern Recognition 1 3.0 3 ★ YANG-LANG CHANG GEE
IEECSG 47 3 ◎

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2020	280215	Pattern Recognition	1	3.0	3	★	YANG-LANG CHANG	GEE IEECSG	47	3	◎

Course Plan：

Instructor YANG-LANG CHANG

Email ylchang@ntut.edu.tw

Last Updated 2020-06-08 13:39:57

Course Objective The course includes: Introduction to Pattern Recognition, Bayesian Decision Theory, Maximum-Likelihood & Bayesian Parameter Estimation, Non-Parametric Classification - Density Estimation, K-Nearest Neighbor Estimation, The Nearest-Neighbor Rule, Multi-Classifier - Positive Boolean Function, Linear Discriminant Functions, Pattern Recognition of High Dimensional Data Sets, Pattern Recognition for Remote Sensing Images, Biometric Certification, Video Surveillance, Accuracy Assessments and Other Related Topics. 本課程介紹電腦科學中圖形識別的基本原理與相關技術，使學生了解各種圖形識別的分類方法，經由對分類問題的分析，瞭解如何將輸入樣本依不同特性加以分類，並熟悉圖形識別系統之相關議題如：分割、特徵擷取、分類、後處理、訓練、監督/非監督式學習、機器學習，進而了解分類問題，增進其解決實際圖形識別分類問題的能力。課程內容包括下列課題：圖形識別概論, Bayesian Decision Theory, Maximum-Likelihood, 非參數法學習, K-Nearest Neighbor Estimation, The Nearest-Neighbor Rule, Positive Boolean Function多分類器, Linear Discriminant Functions，高維資料分類, 分類正確率評估(Accuracy Assessments)及其他重要的應用，例如: 文件分析、生物辨識、影像監控、遙測影像分類、醫療診斷、資料探勘等概念，最後並透過相關論文的研讀及討論，了解近代圖形識別的最新發展趨勢。

Course Schedule
(Week 1-16) 第 1週 W1: Pattern Recognition (PR) Syllabus 第 2週 W2: An introduction to PR and its applications 第 3週 W3: Bayesian Decision Theory (BDT)–Continuous Features 第 4週 W4: BDT–Minimum-Error-Rate Classification 第 5週 W5: BDT–Discrete Features 第 6週 W6: Maximum-Likelihood & Bayesian Parameter Estimation 第 7週 W7: Non-Parametric Classification - Density Estimation 第 8週 W8: Linear Discriminant Functions 第 9週 W9: High Dimensional Data sets (Midterm Exam) 第10週W10: PR of Remote Sensing (Midterm Project Proposal) 第11週W11: PR of Biometric Certification 第12週W12: PR of Video Surveillance 第13週W13: Accuracy Assessment 第14週W14: PR Special Topics in Parallel Computing 第15週W15: PR Special Topics in High Performance Computing 第16週W16: PR Term Project Presentation 第17週W17: PR Term Project Presentation 第18週W18: PR Term Project Presentation (Final Exam)

Evaluation and grading policy 作業、考試成績 Homework Assignments and Exams (50%) 專題 Term project (oral presentation and term paper) (50%)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
參考書 Reference : Pattern Classification by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons

The access to curricular consultation

Expanding teaching and resources

The course corresponds to the SDGs

Does the course incorporate AI

Note
　

Instructor	YANG-LANG CHANG
Email	ylchang@ntut.edu.tw
Last Updated	2020-06-08 13:39:57
Course Objective	The course includes: Introduction to Pattern Recognition, Bayesian Decision Theory, Maximum-Likelihood & Bayesian Parameter Estimation, Non-Parametric Classification - Density Estimation, K-Nearest Neighbor Estimation, The Nearest-Neighbor Rule, Multi-Classifier - Positive Boolean Function, Linear Discriminant Functions, Pattern Recognition of High Dimensional Data Sets, Pattern Recognition for Remote Sensing Images, Biometric Certification, Video Surveillance, Accuracy Assessments and Other Related Topics. 本課程介紹電腦科學中圖形識別的基本原理與相關技術，使學生了解各種圖形識別的分類方法，經由對分類問題的分析，瞭解如何將輸入樣本依不同特性加以分類，並熟悉圖形識別系統之相關議題如：分割、特徵擷取、分類、後處理、訓練、監督/非監督式學習、機器學習，進而了解分類問題，增進其解決實際圖形識別分類問題的能力。課程內容包括下列課題：圖形識別概論, Bayesian Decision Theory, Maximum-Likelihood, 非參數法學習, K-Nearest Neighbor Estimation, The Nearest-Neighbor Rule, Positive Boolean Function多分類器, Linear Discriminant Functions，高維資料分類, 分類正確率評估(Accuracy Assessments)及其他重要的應用，例如: 文件分析、生物辨識、影像監控、遙測影像分類、醫療診斷、資料探勘等概念，最後並透過相關論文的研讀及討論，了解近代圖形識別的最新發展趨勢。
Course Schedule (Week 1-16)	第 1週 W1: Pattern Recognition (PR) Syllabus 第 2週 W2: An introduction to PR and its applications 第 3週 W3: Bayesian Decision Theory (BDT)–Continuous Features 第 4週 W4: BDT–Minimum-Error-Rate Classification 第 5週 W5: BDT–Discrete Features 第 6週 W6: Maximum-Likelihood & Bayesian Parameter Estimation 第 7週 W7: Non-Parametric Classification - Density Estimation 第 8週 W8: Linear Discriminant Functions 第 9週 W9: High Dimensional Data sets (Midterm Exam) 第10週W10: PR of Remote Sensing (Midterm Project Proposal) 第11週W11: PR of Biometric Certification 第12週W12: PR of Video Surveillance 第13週W13: Accuracy Assessment 第14週W14: PR Special Topics in Parallel Computing 第15週W15: PR Special Topics in High Performance Computing 第16週W16: PR Term Project Presentation 第17週W17: PR Term Project Presentation 第18週W18: PR Term Project Presentation (Final Exam)
Evaluation and grading policy	作業、考試成績 Homework Assignments and Exams (50%) 專題 Term project (oral presentation and term paper) (50%)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 參考書 Reference : Pattern Classification by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons
The access to curricular consultation
Expanding teaching and resources
The course corresponds to the SDGs
Does the course incorporate AI
Note