教學大綱表 (114學年度 第1學期)
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課程名稱
Course Title		 (中文) (清大)資料探勘與應用
(英文) Data Mining: Concepts, Techniques, And Applications 		開課單位
Departments		 資訊經營研究所
課程代碼
Course No.		 U2920
授課教師
Instructor		 胡志堅
學分數
Credit		 3.0 必/選修
core required/optional		 選修 開課年級
Level		 研究所
先修科目或先備能力(Course Pre-requisites)：研究所開放大三(含)以上選課; 授課語言: 英文; 建議學生需已修過Python程式設計、有基本機率概念 · 本課程期末專題採分組開發,請審慎評估可投入的時間在選課,若需退選最 晚須於第十週以前退選,以避免影響同組修課同學之權益。
課程概述與目標(Course Overview and Goals)：AI主導課程一:資料探勘與應用(Data Mining : Concepts, Techniques, and Applications)
課程基本資料
開設學校:清華大學
開授教師:陳宜欣老師

Data mining serves as a crucial field that leverages advanced algorithms to reveal hidden, yet invaluable insights buried within extensive datasets. These algorithms are drawn from a multitude of areas such as machine learning, artificial intelligence, pattern recognition, statistics, and database systems, working together to facilitate a deeper understanding and analysis of data.
This course is designed to equip you with the foundational knowledge and hands-on experience needed to delve into the expansive world of data mining. Whether you are looking to enhance your skill set or embark on a new career path, this course will serve as a stepping stone to achieving your goals. The curriculum encompasses a range of topics that will introduce you to the core concepts and
techniques prevalent in the field of data mining. These include:
· Association Rules: Understand the principles behind identifying rules that highlight relationships between seemingly independent data in a database.
· Clustering: Learn about grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
· Classification: Gain knowledge on the procedures for identifying the predefined class of a new observation.
· Text Mining: Equip yourself with the skills needed to analyze and interpret large collections of text data to extract meaningful information.
· Data Mining Applications: Explore the various practical applications of data mining across different industries and sectors.
教科書(Textbook) 課程網頁:https://www.cs.nthu.edu.tw/~yishin/courses/ISA5810/ISA5810-
2025.html
參考教材(Reference) Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining,
Addison Wesley
課程大綱 Syllabus 學生學習目標
Learning Objectives		 單元學習活動
Learning Activities		 學習成效評量
Evaluation		 備註
Notes
序
No.		 單元主題
Unit topic		 內容綱要
Content summary
1 1-Sep Introduction Introduction 演講
 		 
2 8-Sep Overview and Data Overview and Data 講授
 		 
3 15-Sep Overview and Data Overview and Data 討論
講授
 		 
4 15-Sep Lab For Data Exploration And Management (Make up for

Mid-Autumn Festival) 		Lab For Data Exploration And Management (Make up for

Mid-Autumn Festival)		 討論
設計研究
講授
 		 
5 22-Sep Classification Classification 講授
 		 
6 29-Sep Classification Classification 講授
 		 
7 6-Oct Mid-Autumn Festival Mid-Autumn Festival 討論
講授
 		報告
 		 
8 13-Oct Text Mining & Project Progress Report Text Mining & Project Progress Report 講授
 		 
9 20-Oct Lab 2 Lab 2 講授
 		 
10 27-Oct Text Mining Text Mining 講授
 		 
11 3-Nov Text Mining Text Mining 講授
 		 
12 10-Nov DM Clustering DM Clustering 講授
 		 
13 17-Nov DM Clustering & Project Progress Report DM Clustering & Project Progress Report 講授
 		 
14 24-Nov Association Association 講授
 		 
15 1-Dec Student Paper Presentation(同時段同步報告) Student Paper Presentation(同時段同步報告) 討論
講授
心得發表
 		專題
 		 
16 8-Dec Final Exam Final Exam 講授
 		期末考
 		 
17 15-Dec Final Demo Presentation Final Demo Presentation 講授
實作
心得發表
 		報告
專題
 		 


教學要點概述：
1.自編教材 Handout by Instructor：
□ 1-1.簡報 Slids
□ 1-2.影音教材 Videos
□ 1-3.教具 Teaching Aids
□ 1-4.教科書 Textbook
■ 1-5.其他 Other
□ 2.自編評量工具/量表 Educational Assessment
□ 3.教科書作者提供 Textbook

成績考核 Performance Evaluation： 期末考：35%   其他評量：10%   專題：25%   報告：10%   作業：20%  

教學資源(Teaching Resources)：
□ 教材電子檔(Soft Copy of the Handout or the Textbook)
■ 課程網站(Website)
教學相關配合事項：On-line class
課程網站(Website)：課程網頁:https://www.cs.nthu.edu.tw/~yishin/courses/ISA5810/ISA5
扣考規定：https://curri.ttu.edu.tw/p/412-1033-1254.php