Course Syllabus

Course Information：

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

Fall Semester
Year 2025 349846 Data Mining and Business Intelligence 1 3.0 3 ★ Chiu,Chih-Chou GBM 25 0 823

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	349846	Data Mining and Business Intelligence	1	3.0	3	★	Chiu,Chih-Chou	GBM	25	0	823

Course Plan：

Instructor Chiu,Chih-Chou　Office Hours

Email chih3c@ntut.edu.tw

Last Updated 2025-10-19 20:47:02

Course Objective 建立學生對各類資料探勘技術與理論的基礎觀念及架構，以奠定學生日後從事資料探勘分析與研究工作時之需。相關的分析技術將包括應用在分類(classification)、推估(estimation)、預測(prediction)、關聯式分組(affinity grouping)、同質分組(clustering)等功能中的決策樹、(decision tree)多變量分析(multivariate analysis)及類神經網路(neural network)等方法。此外為了讓學生對相關技術有更深的瞭解，課程中將以個案實例為主，並配合各種資料探勘套裝軟體，以應用的觀點使學生更容易接受了解在商業智慧分析上資料探勘分析的功用。 Several fundamental theories of data mining techniques are included in this course. For examples, decision tree, multivariate analysis and neural network. All these techniques will be applied in different areas, such as classification, association rule, forecasting, and pattern recognition. And various applications in different enterprises are also included.

Course Schedule 1 (09/08) Week1 Introduction 2 (09/15) Week2 Illustrated Examples and BI 3 (09/22) Week3 Cluster analysis 1 4 (09/29) Week4 教師 5 (10/06) Week5 中秋 6 (10/13) Week6 Cluster analysis 2 7 (10/20) Week7 Discriminant 1 8 (10/27) Week8 Discriminant 2 9 (11/03) Week9 Discriminant 3 10 (11/10) Week10 Neural Network (MLP) 11 (11/17) Week11 Decision Tree 12 (11/24) Week12 Exam 13 (12/01) Week13 Association Rule 14 (12/08) Week14 Project discussion 15 (12/15) Week15 Project discussion 16 (12/22) Week16 Project discussion 17 (12/29) Week17 Project discussion 18 (01/05) Week18 Project presentation

Evaluation and grading policy Exam 40% Final Project 60%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
● 資料探勘 (Data Mining), 曾憲雄等, 旗標出版股份有限公司, 2005 ● Machine learning, Tom Mitchell, McGraw-Hill, 1997 ● Data Mining: Concepts and Techniques, Han and Kamber, Morgan Kaufmann, 2000 ● Predictive Data Mining, Weiss and Indurkhya, Morgan Kaufmann, 1998 ● Data Mining Your Web Site, J. Mena, Digital Press, 1999 ● Research paper collection, (collected by instructor) will be distributed in class ● Applied Multivariate Statistical Analysis, by Johnson, R.A., and Wichern, D.W., Prentice Hall, 2002. ● Machine Learning Group at the University of Waikato https://www.cs.waikato.ac.nz/ml/weka/courses.html

The access to curricular consultation 課程諮詢管道
1. Office hour 詢問
2. Google meet 遠距討論
3. Line group 群組訊息分享及討論

Expanding teaching and resources

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

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）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Chiu,Chih-Chou　Office Hours
Email	chih3c@ntut.edu.tw
Last Updated	2025-10-19 20:47:02
Course Objective	建立學生對各類資料探勘技術與理論的基礎觀念及架構，以奠定學生日後從事資料探勘分析與研究工作時之需。相關的分析技術將包括應用在分類(classification)、推估(estimation)、預測(prediction)、關聯式分組(affinity grouping)、同質分組(clustering)等功能中的決策樹、(decision tree)多變量分析(multivariate analysis)及類神經網路(neural network)等方法。此外為了讓學生對相關技術有更深的瞭解，課程中將以個案實例為主，並配合各種資料探勘套裝軟體，以應用的觀點使學生更容易接受了解在商業智慧分析上資料探勘分析的功用。 Several fundamental theories of data mining techniques are included in this course. For examples, decision tree, multivariate analysis and neural network. All these techniques will be applied in different areas, such as classification, association rule, forecasting, and pattern recognition. And various applications in different enterprises are also included.
Course Schedule	1 (09/08) Week1 Introduction 2 (09/15) Week2 Illustrated Examples and BI 3 (09/22) Week3 Cluster analysis 1 4 (09/29) Week4 教師 5 (10/06) Week5 中秋 6 (10/13) Week6 Cluster analysis 2 7 (10/20) Week7 Discriminant 1 8 (10/27) Week8 Discriminant 2 9 (11/03) Week9 Discriminant 3 10 (11/10) Week10 Neural Network (MLP) 11 (11/17) Week11 Decision Tree 12 (11/24) Week12 Exam 13 (12/01) Week13 Association Rule 14 (12/08) Week14 Project discussion 15 (12/15) Week15 Project discussion 16 (12/22) Week16 Project discussion 17 (12/29) Week17 Project discussion 18 (01/05) Week18 Project presentation
Evaluation and grading policy	Exam 40% Final Project 60%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No ● 資料探勘 (Data Mining), 曾憲雄等, 旗標出版股份有限公司, 2005 ● Machine learning, Tom Mitchell, McGraw-Hill, 1997 ● Data Mining: Concepts and Techniques, Han and Kamber, Morgan Kaufmann, 2000 ● Predictive Data Mining, Weiss and Indurkhya, Morgan Kaufmann, 1998 ● Data Mining Your Web Site, J. Mena, Digital Press, 1999 ● Research paper collection, (collected by instructor) will be distributed in class ● Applied Multivariate Statistical Analysis, by Johnson, R.A., and Wichern, D.W., Prentice Hall, 2002. ● Machine Learning Group at the University of Waikato https://www.cs.waikato.ac.nz/ml/weka/courses.html
The access to curricular consultation	課程諮詢管道 1. Office hour 詢問 2. Google meet 遠距討論 3. Line group 群組訊息分享及討論
Expanding teaching and resources
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
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） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note