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
111-2	315970	資料探勘	1	3.0	3	★	王正豪 沐海	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

教學大綱與進度：

教師姓名	王正豪　教師諮商時間(Office Hours)
Email	jhwang@ntut.edu.tw
最後更新時間	2022-12-20 10:56:49
課程大綱	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.
課程進度	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)
評量方式與標準	Homework assignments and programming exercises: ~30-35% Mid-term exam: ~30-35% Term Project: ~30-35%
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 - 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/
課程諮詢管道	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.
課程對應SDGs指標
備註	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.