Course Syllabus

Course Information：

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

Spring Semester
Year 2026 359329 Data Mining 1 3.0 3 ★ HUANG YO-PING GEE
IEECSG 16 0 Jointly offered by Electrical Engineering department.

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	359329	Data Mining	1	3.0	3	★	HUANG YO-PING	GEE IEECSG	16	0	Jointly offered by Electrical Engineering department.

Course Plan：

Instructor HUANG YO-PING　Office Hours

Email yphuang@ntut.edu.tw

Last Updated 2025-12-17 13:51:38

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. 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 Lecture notes will be available to download from iPlus.

The access to curricular consultation We have office hours, line communication, email, etc for curricular consultation.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG5：性別平等（Gender Equality）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate 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）

Note
1. Contact email: yphuang@ntut.edu.tw
2. Just in case we need to have virtual classes, then we will use Google meet:
https://meet.google.com/mkg-yrsk-bss

Instructor	HUANG YO-PING　Office Hours
Email	yphuang@ntut.edu.tw
Last Updated	2025-12-17 13:51:38
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. 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 Lecture notes will be available to download from iPlus.
The access to curricular consultation	We have office hours, line communication, email, etc for curricular consultation.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG5：性別平等（Gender Equality） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate 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）
Note	1. Contact email: yphuang@ntut.edu.tw 2. Just in case we need to have virtual classes, then we will use Google meet: https://meet.google.com/mkg-yrsk-bss