Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	347338	Big Data Mining and Applications	1	3.0	3	★	Jenq-Haur Wang	4CSIE4	15	0	資工四、資工所、電資外生所和創新AI、資安合開

Course Plan：

Instructor	Jenq-Haur Wang　Office Hours
Email	jhwang@ntut.edu.tw
Last Updated	2025-05-27 14:21:49
Course Objective	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.
Course Schedule	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)
Evaluation and grading policy	Homework assignments and programming exercises: ~40% Mid-term exam: ~25-30% Term project and presentation: ~30-35%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 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.
The access to curricular consultation	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/
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	AI5：深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	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.