NCHU Course Outline
Course Name (中) 資料探勘概論(2160)
(Eng.) Introduction to Data Mining
Offering Dept Department of Management Information Systems
Course Type Required Credits 3 Teacher TSAI, MENG-HSIUN
Department Department of Management Information Systems / Undergraduate Language Chinese Semester 2026-SPRING
Course Description 資料探勘(Data mining)，是資料庫知識發現(knowledge-discovery in databases , KDD)中的一個重要步驟，是指運用特定的演算法從大量的資料庫，以可接受的計算頻率內中去萃取有意義，具有潛在價值的資訊、規則或是pattern進而做出正確的決策的一個步驟。本課程內容介紹數種具有代表性的演算法，幫助學生了解如何運用資料分析技術與統計方法，尋找資料的關聯性、擷取隱藏的資訊規則與進行預測。

【注意】 請在第一次上課前，透過課程平台入口，申請好學校的 smail。且第一堂課務必實體抵達課堂，以便後續課程教學平台操作，如第一堂課程未能前往教室，請務必事前寄信通知課程 TA。
Prerequisites
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
基本與進階之資料探勘技術介紹、應用實例以及軟體工具的使用和專案實作
Basic and advanced introduction to data mining techniques, applications, software usage and implementation
1.Professional Knowledge with Applications
2.Independent Thinking
3.Creativity
4.English Proficiency
30
30
20
20
topic Discussion / Production
Networking / Distance Education
Exercises
Lecturing
Attendance
Oral Presentation
Assignment
Quiz
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 1. Introduction
Week 2 2. Data mining & Knowledge discovery (Statistics for Data Mining)
Week 3 3. CRISP-DM , Data Visualization, UCI & Kaggle
Week 4 4. Data Preprocessing 1 - Data Cleaning & Data integration
Week 5 5. Data Preprocessing 2 - Data Transformation & Feature selection

Week 6 6. Classify I : Decision Trees
Week 7 7. 調整放假
Week 8 8. Classify II : Ensemble and Bayesian
Week 9 9. Classify III : Artificial Neural Network

Week 10 10. 5-min pre-final report
Week 11 11. Associations : Apriori , FP-growth
Week 12 12. Clustering : K-Means, Hierarchical Cluster
Week 13 13. Midterm Exam
Week 14 14. Final report presentation - 1
Week 15 15. Final report presentation - 2
Week 16 16. Final report presentation - 3
self-directed
learning		    02.Viewing multimedia materials related to industry and academia.
Evaluation
1.課堂練習 20%

2.期中考 30%

3.期末報告 40%

4.出席率 10%
Textbook & other References
主要必備書目:
1.Fundamentals of Machine Learning for Data Analytics(Algorithms,Worked Examples,And Case Studies),Second Edition,著.John D.Kelleher,Brian Mac Namee,Aoife D’Arcy

2.Introduction to Data Mining (GE),著.Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar


參考書目:
1.資料挖礦與大數據分析, 簡禎富, 許嘉裕, ISBN:978-9865774257, 前程文化事業有限公司

2.王者歸來:WEKA機器學習與大數據聖經,2015,袁梅宇,佳魁資訊出版

3.Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition, Jiawei Han, Micheline Kamber, Jian Pei, ISBN:978-9380931913, Morgan Kaufmann

4.資料探勘,Pang-Ning Tan,Michael Steinbach,Vipin Kumar 著,施雅月,賴錦慧 譯, ISBN:978-9861546575, 臺灣培生教育出版股份有限公司

5. Data Mining : Practical Machine Learning Tools and Techniques, Ian H. , Eibe Frank , Mark Hall ,
Christopher Pal

6. R資料採礦與數據分析,何宗武,碁峯圖書

7. R 語⾔資料分析活⽤範例詳解,⽅匡南,朱建平,姜葉⾶,碁峰圖書

8. Python⼤數據特訓班：資料⾃動化收集、整理、分析、儲存與應⽤實戰,文淵閣⼯作室編著,碁峰
圖書

9. Python機器學習,Sebastian Raschka著,劉立⺠，吳建華譯,博碩文化

10. 機器學習：使⽤Python進⾏預測分析的基本技術 Machine Learning in Python: Essential Techniques
for Predictive Analysis,Michael Bowles,碁峰圖書

11. Python機器學習(第三版)-上, Sebastian Raschka、Vahid Mirjalili, ISBN: 9789864345182, 博碩文化

12. Python機器學習(第三版)-下, Sebastian Raschka、Vahid Mirjalili, ISBN: 9789864345182, 博碩文化

13. 統計學與Excel資料分析之實習應用〈第七版〉[培養大數據分析力一定要會的統計分析與資料處理工具], 王文中、錢才瑋, ISBN: 9789864348497 ,博碩文化

14. 統計學 (第11版), Gerald Keller, ISBN: 9789579282369 ,新加坡商聖智學習亞洲私人有限公司台灣分公司

15. 機器學習入門：使用Scikit-Learn與TensorFlow, 黃健庭, ISBN: 9786263240285, 碁峰資訊

16. Python Data Science Bible資料科學自學聖經, 文淵閣工作室, ISBN: 9786263241657, 碁峰資訊

17. Python 機器學習超進化：AI影像辨識跨界應用實戰, 文淵閣工作室, ISBN: 9789865026196, 碁峰資訊
Teaching Aids & Teacher's Website
http://mht.mis.nchu.edu.tw/moodle/
Office Hours
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Update Date, year/month/day：2026/02/04 13:58:05 Printed Date, year/month/day：2026 / 5 / 31
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