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

110-2 301359 資料探勘 1 3.0 3 ★ 沐海
劉建宏 AI雙聯專班一 35 0 110碩一要修，5/14、5/28、6/12、6/18、6/19、6/25

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
110-2	301359	資料探勘	1	3.0	3	★	沐海劉建宏	AI雙聯專班一	35	0	110碩一要修，5/14、5/28、6/12、6/18、6/19、6/25

教學大綱與進度：

教師姓名劉建宏

Email cliu@ntut.edu.tw

最後更新時間 2022-01-25 08:54:34

課程大綱資料探勘是從大量資料中尋找有用的資訊，是一個跨越統計、資料庫和人工智慧的學門。本課程著重在從資料庫的觀點來看資料探勘的意義，並深入探討幾種資料探勘功能及其技術，最後並以實例討論來驗證資料探勘的應用價值。探討議題包括資料探勘概念、資料探勘功能、資料探勘步驟、資料倉儲、決策樹分析集群分析、關聯分析與購物籃分析等。 The data mining (DM), which involves in Statistics, Database and Artificial Intelligence fields, prospects to seek Useful information from a great deal of data. This course emphasizes at the view of the database, besides go deeply into several data mining techniques. Finally, the practical case studies will also be discussed. The topic includes the concept of DM, the function of DM, the step of DM, data warehouse, Decision Trees Analysis, Clustering Analysis, Association Rule and Basket Analyzes, etc.

課程進度 1. Introduction to Data Mining 2. Data mining applications 3. Data Processing 4. Data Warehousing 5. Data Processing 6. Mining Frequent Patterns, Associations, and Correlations 7. Classification 8. Cluster Analysis 9. Outlier Detection

評量方式與標準 1. Homework assignments, paper reviews (25%) 2. Participation (20%) 3. Midterm exam (25%) 4. Final Project (30%)

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：是
Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd Ed., Morgan Kaufmann Publishers 2012.

課程諮詢管道

備註
The course will be offered online using MS Teams during the period of Level 3 COVID-19 Alert. The Teams link created by school will be used. Check https://myweb.ntut.edu.tw/~cliu/courses/dm/dm.htm or email instructor for details.

教師姓名	劉建宏
Email	cliu@ntut.edu.tw
最後更新時間	2022-01-25 08:54:34
課程大綱	資料探勘是從大量資料中尋找有用的資訊，是一個跨越統計、資料庫和人工智慧的學門。本課程著重在從資料庫的觀點來看資料探勘的意義，並深入探討幾種資料探勘功能及其技術，最後並以實例討論來驗證資料探勘的應用價值。探討議題包括資料探勘概念、資料探勘功能、資料探勘步驟、資料倉儲、決策樹分析集群分析、關聯分析與購物籃分析等。 The data mining (DM), which involves in Statistics, Database and Artificial Intelligence fields, prospects to seek Useful information from a great deal of data. This course emphasizes at the view of the database, besides go deeply into several data mining techniques. Finally, the practical case studies will also be discussed. The topic includes the concept of DM, the function of DM, the step of DM, data warehouse, Decision Trees Analysis, Clustering Analysis, Association Rule and Basket Analyzes, etc.
課程進度	1. Introduction to Data Mining 2. Data mining applications 3. Data Processing 4. Data Warehousing 5. Data Processing 6. Mining Frequent Patterns, Associations, and Correlations 7. Classification 8. Cluster Analysis 9. Outlier Detection
評量方式與標準	1. Homework assignments, paper reviews (25%) 2. Participation (20%) 3. Midterm exam (25%) 4. Final Project (30%)
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：是 Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd Ed., Morgan Kaufmann Publishers 2012.
課程諮詢管道
備註	The course will be offered online using MS Teams during the period of Level 3 COVID-19 Alert. The Teams link created by school will be used. Check https://myweb.ntut.edu.tw/~cliu/courses/dm/dm.htm or email instructor for details.