Course Syllabus

Course Information：

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

Spring Semester
Year 2021 285768 Data Mining 1 3.0 3 ★ HUANG YO-PING GEE
IEECSG 16 2 ◎

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2021	285768	Data Mining	1	3.0	3	★	HUANG YO-PING	GEE IEECSG	16	2	◎

Course Plan：

Instructor HUANG YO-PING

Email yphuang@ntut.edu.tw

Last Updated 2020-12-14 10:33:50

Course Objective Scope: This course is to introduce useful data mining techniques for students. We hope that students can learn more fundamental and advanced skills in the competitive mobile commerce world. “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets. Data mining (knowledge discovery from data) : Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data.

Course Schedule 1: Introduction 2~3: Data preprocessing 4~6: Mining association rules in large databases 7~8: Classification and prediction 9: Mid-term exam 10~11: Cluster analysis 12~13: Mining complex types of data 14~15: Spatial and temporal data mining 16~17: Advanced topics in data mining 18: Final exam

Evaluation and grading policy 1. Mid-term 30% 2. Final-exam 30% 3. Quizzes+HWs 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook: J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Reference: M.H. Dunham, Data mining: introductory and advanced topics, 歐亞書局代理

The access to curricular consultation

Note
　

Instructor	HUANG YO-PING
Email	yphuang@ntut.edu.tw
Last Updated	2020-12-14 10:33:50
Course Objective	Scope: This course is to introduce useful data mining techniques for students. We hope that students can learn more fundamental and advanced skills in the competitive mobile commerce world. “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets. Data mining (knowledge discovery from data) : Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data.
Course Schedule	1: Introduction 2~3: Data preprocessing 4~6: Mining association rules in large databases 7~8: Classification and prediction 9: Mid-term exam 10~11: Cluster analysis 12~13: Mining complex types of data 14~15: Spatial and temporal data mining 16~17: Advanced topics in data mining 18: Final exam
Evaluation and grading policy	1. Mid-term 30% 2. Final-exam 30% 3. Quizzes+HWs 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook: J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Reference: M.H. Dunham, Data mining: introductory and advanced topics, 歐亞書局代理
The access to curricular consultation
Note