教學大綱 Syllabus

科目名稱：資料探勘與應用

Course Name: Data Mining : Concepts, Techniques, and Applications
  • 學年學期：114-1 Fall Semester, 2025
  • 科目代碼：070421001 Course No.070421001

修別：必

Type of Credit: Required

3.0

學分數

Credit(s)

45

預收人數

Number of Students

課程資料Course Details
  • 開課單位：人智應學士 Course Department:Artificial Intelligence Applications
  • 授課老師：詳備註 Instructor: Ref. Note
  • 先修科目：無Prerequisite(N/A)
  • 上課時間： Session:

課程簡介Course Description

  1. 本課程為臺灣大專院校人工智慧學程聯盟所開課之主導課程，授課老師為清大陳宜欣老師；課程管理及進行均依授課老師指定方式進行。同學請審慎評估課程內容難易程度，並留意課程所列之同步上課/考試/報告時間。
  2. 本課程採遠距教學方式上課，使用NTU COOL平台；課程同步遠距時間為週一9:00-12:00，可接受非同步授課。
  3. 實體期末評量時間：2025年12月8日（所有學生需同步進行，在此時段無法應考的學生請勿修課）
  4. 課程聯繫均透過個人政大信箱(學號@nccu.edu.tw)，修課同學務必留意信件。選課同學選上課約一週後，個人政大信箱(學號@nccu.edu.tw)將會收到NTU COOL平台的加入邀請，依指示操作即加入；如同學加入課程的時間較晚，可自行於平台上觀看之前的上課影片。
  5. 本課程不開放加簽；期中停修需依聯盟公告之截止日期及申請方式辦理。
  6. 本課程對應之學分學程為人工智慧自然語言處理學分學程（資料探勘與應用）。
  7. 本課程性質為碩士班(開放大三以上選課)，授課語言為英文，是否可認列為各系所(學程)之畢業學分，請逕洽各系所單位。
  8. 課程要求：
    • 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再選課，若需退選最晚需於第10週以前退選，以避免影響同組修課同學之權益。

 

  1. This Course is offered by Taiwan Artificial Intelligence College Alliance (TAICA). The instructor for this course is Professor Yi-Shin Chen from NTHU. Course management and execution will follow the methods designated by the instructor. Students are advised to carefully assess the difficulty level of the course and make sure can join the specific class time according to the course guideline, such as the final exam.
  2. This is an Synchronous Distance course. The class time is on Mon 9:00-12:00 AM. Students can either join the class in time or view the video after the class.
  3. The final in-person exam will be held on December 08, 2025.
  4. All the information of this course will be sent to student’s school email box ( student ID number@nccu.edu.tw). Please make sure to check your school email frequently.  The Students will need to use the “NTU COOL” platform and will receive the account information of NTU COOL one week after joining the course.
  5. Manual Adding is NOT allowed.
  6. The course is for Master student and is open to B3/B4.
  7. It is recommended that students have studied Python programming and have basic probability concepts.
  8. The final project will be conducted in groups. Please carefully evaluate the time you can devote before taking this course to avoid affecting the rights and interests of other students in the same group.
  9. The deadline of Course withdrawal weill follow the TAICA OFFICE's announcemnet and will be no later than Week 10.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明

    課程目標與學習成效Course Objectives & Learning Outcomes

    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.

    每周課程進度與作業要求 Course Schedule & Requirements

    Week

    Date

    Topic

    Note

    1

    1-Sep

    Introduction

     

    2

    8-Sep

    Overview and Data

     

    3

    15-Sep

    Overview and Data

     

    3

    15-Sep

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

     

    4

    22-Sep

    Classification

     

    5

    29-Sep

    Classification

    本週不直播上課，將有課程錄影與學習進度，請學生自行學習。

    6

    6-Oct

    Mid-Autumn Festival

     
    7 13-Oct Text Mining & Project Progress Report  

    8

    20-Oct

    Lab 2

     

    9

    27-Oct

    Text Mining

     

    10

    3-Nov

    Text Mining

     

    11

    10-Nov

    DM Clustering

     

    12

    17-Nov

    DM Clustering & Project Progress Report

     

    13

    24-Nov

    Association

     

    14

    1-Dec

    Student Paper Presentation（同時段同步報告）

     

    15

    8-Dec

    Final Exam

     

    16

    15-Dec

    Final Demo Presentation

     

    授課方式Teaching Approach

    80%

    講述 Lecture

    20%

    討論 Discussion

    0%

    小組活動 Group activity

    0%

    數位學習 E-learning

    0%

    其他： Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    • Two assignments: 20%
    • One short presentation: 10%
    • One project: 25%
    • One exam: 35%
    • Class participation (in or after class): 10%

    指定/參考書目Textbook & References

    Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

    維護智慧財產權，務必使用正版書籍。 Respect Copyright.

    本課程可否使用生成式AI工具Course Policies on the Use of Generative AI Tools

    有條件開放使用:依授課教師規定 Conditional Permitted to Use

    課程相關連結Course Related Links

    課程名稱：資料探勘與應用
授課教師：清大陳宜欣老師
遠距上課位置：https://www.youtube.com/@NTHU_ISA5810_DataMining
課程網頁：https://www.cs.nthu.edu.tw/~yishin/courses/ISA5810/ISA5810-2025.html
NTUCOOL平台：cool.ntu.edu.tw

    課程附件Course Attachments

    課程進行中，使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    需經教師同意始得使用 Approval

    列印