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

114-1 347338 巨量資料探勘與應用 1 3.0 3 ★ 王正豪資工四 15 3 資工四、資工所、電資外生所和創新AI、資安合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-1	347338	巨量資料探勘與應用	1	3.0	3	★	王正豪	資工四	15	3	資工四、資工所、電資外生所和創新AI、資安合開

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 2025-05-27 14:21:49

課程大綱 This course is a graduate-level course for advanced big data analytics and applications. The major topics include: the concepts and principles of big data analytics, frameworks of distributed computing, and applications including: Link Analysis, Social Network Graph Mining, and Recommender Systems. In addition to conceptual introduction, with intensive programming exercises in parallel programming and term project, students are expected to learn the big data analytics tools, and to design programs in real big data applications.

課程進度 Part I: Big Data Analytics concepts & Distributed processing (8 wks) 1. Introduction to big data concepts (1 wk) 2. Introduction to distributed platforms: Hadoop & Spark (1 wk) 3. MapReduce programming: design patterns (pairs & stripes), language model (1 wk) 4. Demo: big data analytics using distributed platforms: Hadoop & Spark (1 wk) 5. Finding similar items: LSH (2 wks) 6. Clustering (1 wk) 7. Dimensionality reduction (1 wk) Midterm exam (1 wk) Part II: Applications of Big Data Analytics (6 wks) 1. Recommender Systems (1 wk) 2. Link Analysis (2 wks) 3. Social Network Graph Mining (2 wks) 4. Others: Large-scale machine learning (1 wk) Term project presentation (3 wks)

評量方式與標準 Homework assignments and programming exercises: ~40% Mid-term exam: ~25-30% Term project and presentation: ~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: 1. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, 3rd edition, Cambridge University Press, 2020. References: 1. Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining: Concepts and Techniques, 4th edition, Morgan Kaufmann, 2022. 2. Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer. Morgan & Claypool Publishers, 2010. 3. Recent academic papers, tutorials, and slides.

課程諮詢管道 You can reach the teacher by appointment via e-mail.
All course materials and video recordings will be kept in Microsoft Teams and iSchool+, and the course web site at: https://chriswjh.github.io/BDM/

延伸教學與資源

課程對應SDGs指標 SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

課程是否導入AI ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

備註 Course information will be available at: iSchool+, Microsoft Teams, and my course website at: https://chriswjh.github.io/BDM/ If you are unable to attend the class in person, you can join the class online or watch recorded sessions via Microsoft Teams. If you haven't been added to the team of this course, please send e-mails to me at: jhwang@ntut.edu.tw. (Remember to identify the course name, your name and student ID.) The TA will establish additional communication channels (e.g. Facebook) after students have enrolled in this class.

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	2025-05-27 14:21:49
課程大綱	This course is a graduate-level course for advanced big data analytics and applications. The major topics include: the concepts and principles of big data analytics, frameworks of distributed computing, and applications including: Link Analysis, Social Network Graph Mining, and Recommender Systems. In addition to conceptual introduction, with intensive programming exercises in parallel programming and term project, students are expected to learn the big data analytics tools, and to design programs in real big data applications.
課程進度	Part I: Big Data Analytics concepts & Distributed processing (8 wks) 1. Introduction to big data concepts (1 wk) 2. Introduction to distributed platforms: Hadoop & Spark (1 wk) 3. MapReduce programming: design patterns (pairs & stripes), language model (1 wk) 4. Demo: big data analytics using distributed platforms: Hadoop & Spark (1 wk) 5. Finding similar items: LSH (2 wks) 6. Clustering (1 wk) 7. Dimensionality reduction (1 wk) Midterm exam (1 wk) Part II: Applications of Big Data Analytics (6 wks) 1. Recommender Systems (1 wk) 2. Link Analysis (2 wks) 3. Social Network Graph Mining (2 wks) 4. Others: Large-scale machine learning (1 wk) Term project presentation (3 wks)
評量方式與標準	Homework assignments and programming exercises: ~40% Mid-term exam: ~25-30% Term project and presentation: ~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: 1. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, 3rd edition, Cambridge University Press, 2020. References: 1. Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining: Concepts and Techniques, 4th edition, Morgan Kaufmann, 2022. 2. Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer. Morgan & Claypool Publishers, 2010. 3. Recent academic papers, tutorials, and slides.
課程諮詢管道	You can reach the teacher by appointment via e-mail. All course materials and video recordings will be kept in Microsoft Teams and iSchool+, and the course web site at: https://chriswjh.github.io/BDM/
延伸教學與資源
課程對應SDGs指標	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
課程是否導入AI	● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
備註	Course information will be available at: iSchool+, Microsoft Teams, and my course website at: https://chriswjh.github.io/BDM/ If you are unable to attend the class in person, you can join the class online or watch recorded sessions via Microsoft Teams. If you haven't been added to the team of this course, please send e-mails to me at: jhwang@ntut.edu.tw. (Remember to identify the course name, your name and student ID.) The TA will establish additional communication channels (e.g. Facebook) after students have enrolled in this class.