Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2023	315970	Data Mining	1	3.0	3	★	Jenq-Haur Wang Mohammad	AI雙聯專班	26	0	應用組，碩一修，3.19 4.8 4.16 5.7 5.21 6.3

Course Plan：

Instructor	Mohammad
Email	muhai@ntut.edu.tw
Last Updated	2022-12-22 17:03:35
Course Objective	This course introduces the methods and applications of data mining. The topics include data management, data mining techniques, big data platforms, and project implementation and presentation. Students are expected to learn the concepts and techniques of data mining algorithms, and their applications and future trends in different case studies.
Course Schedule	Part I: Data mining concepts (7 wks) 1. Introduction to data mining & data preprocessing (1 wk) 2. Frequent pattern mining (2 wks) 3. Data classification (2 wks) 4. Data clustering (1 wk) 5. Midterm exam (1 wk) Part II: Distributed processing (5 wks) 1. Introduction to distributed platforms: Hadoop & Spark (1 wk) 2. MapReduce programming: relational algebra, pairs & stripes (1 wk) 3. Case study of Spark programming: frequent itemset, classification, clustering (2 wks) 4. Term Project presentation (1 wk)
Evaluation and grading policy	Homework assignments and programming exercises: ~30-35% Mid-term exam: ~30-35% Term Project: ~30-35%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes - Textbook: Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011. - References: 1. Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010. 2. Holden Karau, andy Konwinski, Patrick Wendell, and Matei Zaharia, Learning Spark: Lightning-fast data analysis, O'Reilly, 2015. 3. Recent academic papers, tutorials, and slides. - Latest news available soon at: https://chriswjh.github.io/DM/
The access to curricular consultation	The teacher can be reached via e-mails or messages in Microsoft Teams. The TA will help you join the course group in Facebook where you can also ask questions about the homeworks and exams.
The course corresponds to the SDGs
Does the course incorporate AI
Note	All course sessions will be recorded and put on iSchool+ afterwards, along with all course materials, homework announcements and submissions. To prepare for further impacts of COVID-19 pandemic, the following channels will be set up: 1. Microsoft Teams: you will be automatically added to the team for your enrolled courses in Teams. When the course is held online, please join our class with your Teams ID (StudentID@cc.ntut.edu.tw). 2. Facebook: we will set up a group for the course in Facebook where you can also ask questions about the homeworks and exams.