Course Syllabus

Course Information：

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

Fall Semester
Year 2026 366984 Pattern Recognition 1 3.0 3 ★ YANG-LANG CHANG
Tung-Ju Hsieh AI雙聯專班 11 0 碩二修

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366984	Pattern Recognition	1	3.0	3	★	YANG-LANG CHANG Tung-Ju Hsieh	AI雙聯專班	11	0	碩二修

Course Plan：

Instructor YANG-LANG CHANG　Office Hours

Email ylchang@ntut.edu.tw

Last Updated 2026-07-07 11:34:31

Course Objective 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。 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.

Course Schedule 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 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 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: Accuracy Assessment 第12週W12: PR of Video Surveillance 第13週W13: PR Special Topics in Parallel Computing 第14週W14: PR Special Topics in High Performance Computing 第15週W15: PR Term Project Presentation 第16週W16: PR Term Project Presentation 第17週W17: PR Independent Study 第18週W18: PR Independent Study

Evaluation and grading policy 作業成績 Homework Assignments (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 課程諮詢及最新資訊公布: 請申請(經助教認證後)加入FB"北科電研圖形識別-Pattern Recognition" https://www.facebook.com/groups/1468628966800395

Expanding teaching and resources ● 數位教材資源應用 (Application of Digital Resources)
採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。
Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）
SDG13：氣候行動（Climate Action）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）

Note
　

Instructor	YANG-LANG CHANG　Office Hours
Email	ylchang@ntut.edu.tw
Last Updated	2026-07-07 11:34:31
Course Objective	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。 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.
Course Schedule	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 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 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: Accuracy Assessment 第12週W12: PR of Video Surveillance 第13週W13: PR Special Topics in Parallel Computing 第14週W14: PR Special Topics in High Performance Computing 第15週W15: PR Term Project Presentation 第16週W16: PR Term Project Presentation 第17週W17: PR Independent Study 第18週W18: PR Independent Study
Evaluation and grading policy	作業成績 Homework Assignments (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	課程諮詢及最新資訊公布: 請申請(經助教認證後)加入FB"北科電研圖形識別-Pattern Recognition" https://www.facebook.com/groups/1468628966800395
Expanding teaching and resources	● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities） SDG13：氣候行動（Climate Action）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note