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

112-1 321524 巨量資料探勘與應用 1 3.0 3 ★ 王正豪資工所
電資外生所
人工智慧學位學程
創新AI學位學程 53 10 電資外生所、資工四、資工所和電資AI、創新AI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
112-1	321524	巨量資料探勘與應用	1	3.0	3	★	王正豪	資工所電資外生所人工智慧學位學程創新AI學位學程	53	10	電資外生所、資工四、資工所和電資AI、創新AI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 2023-05-29 17:21:56

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. The commonly used machine learning methods such as artificial neural networks and SVM will also be covered. In addition to conceptual introduction, with intensive programming exercises in parallel programming and term project, students are expected to learn the machine learning tools, and the program design of real big data applications.

課程進度 Part I: Big Data Analytics concepts & Distributed processing (7 wks) 1. Introduction to big data concepts (1 wk) 2. Introduction to distributed platforms: Hadoop & Spark (1 wk) 3. MapReduce programming: pairs & stripes, relational algebra (1 wk) 4. More examples: matrix multiplication, similarity (1 wk) 5. Machine Learning Methods: NN, SVM (3 wks) Midterm exam (1 wk) Part II: Applications of Big Data Anlaytics (7 wks) 1. Finding Similar Items (1 wk) 2. Mining Data Streams (1 wk) 3. Link Analysis (1 wk) 4. Frequent Itemset (1 wk) 5. Recommendation Systems (1 wk) 6. Social Network Graph Mining (1 wk) 7. Others: Dimensionality Reduction (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, 3rd 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 via e-mail or by appointment.
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/

備註
Please check the course web site at: https://chriswjh.github.io/BDM/

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	2023-05-29 17:21:56
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. The commonly used machine learning methods such as artificial neural networks and SVM will also be covered. In addition to conceptual introduction, with intensive programming exercises in parallel programming and term project, students are expected to learn the machine learning tools, and the program design of real big data applications.
課程進度	Part I: Big Data Analytics concepts & Distributed processing (7 wks) 1. Introduction to big data concepts (1 wk) 2. Introduction to distributed platforms: Hadoop & Spark (1 wk) 3. MapReduce programming: pairs & stripes, relational algebra (1 wk) 4. More examples: matrix multiplication, similarity (1 wk) 5. Machine Learning Methods: NN, SVM (3 wks) Midterm exam (1 wk) Part II: Applications of Big Data Anlaytics (7 wks) 1. Finding Similar Items (1 wk) 2. Mining Data Streams (1 wk) 3. Link Analysis (1 wk) 4. Frequent Itemset (1 wk) 5. Recommendation Systems (1 wk) 6. Social Network Graph Mining (1 wk) 7. Others: Dimensionality Reduction (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, 3rd 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 via e-mail or by appointment. 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/
備註	Please check the course web site at: https://chriswjh.github.io/BDM/