查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

114-1 349846 資料探勘與商業智慧 1 3.0 3 ★ 邱志洲經管所 25 0 823

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-1	349846	資料探勘與商業智慧	1	3.0	3	★	邱志洲	經管所	25	0	823

教學大綱與進度：

教師姓名邱志洲　教師諮商時間(Office Hours)

Email chih3c@ntut.edu.tw

最後更新時間 2025-10-19 20:47:02

課程大綱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.

課程進度 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

評量方式與標準 Exam 40% Final Project 60%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：否
● 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

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

延伸教學與資源

課程對應SDGs指標 SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入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）

備註

教師姓名	邱志洲　教師諮商時間(Office Hours)
Email	chih3c@ntut.edu.tw
最後更新時間	2025-10-19 20:47:02
課程大綱	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.
課程進度	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
評量方式與標準	Exam 40% Final Project 60%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：否 ● 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
課程諮詢管道	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
延伸教學與資源
課程對應SDGs指標	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入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）
備註