Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2018 241565 Big Data Mining and Applications 1 3.0 3 ★ Jenq-Haur Wang 4CSIE4 28 5 資工四與資工所、電資外生班合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2018	241565	Big Data Mining and Applications	1	3.0	3	★	Jenq-Haur Wang	4CSIE4	28	5	資工四與資工所、電資外生班合開

Course Plan：

Instructor Jenq-Haur Wang

Email jhwang@ntut.edu.tw

Last Updated 2017-12-25 18:09:00

Course Objective 本課程是研究所程度進階的巨量資料探勘與應用課程，主要介紹巨量資料探勘概念和原理、分散式計算基本架構、及各種應用包括: Link Analysis、Social Network Graph Mining、推薦系統等, 並且將介紹在處理巨量資料時常用的機器學習方法, 如: 類神經網路、SVM等，除了觀念的介紹外，透過平行化程式設計的練習以及期末專題，能讓學生認識實際巨量資料探勘的程式設計以及機器學習等工具的應用方式。

Course Schedule 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)

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, 2nd edition, Cambridge University Press, 2014. References: 1. Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011. 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

Note
　

Instructor	Jenq-Haur Wang
Email	jhwang@ntut.edu.tw
Last Updated	2017-12-25 18:09:00
Course Objective	本課程是研究所程度進階的巨量資料探勘與應用課程，主要介紹巨量資料探勘概念和原理、分散式計算基本架構、及各種應用包括: Link Analysis、Social Network Graph Mining、推薦系統等, 並且將介紹在處理巨量資料時常用的機器學習方法, 如: 類神經網路、SVM等，除了觀念的介紹外，透過平行化程式設計的練習以及期末專題，能讓學生認識實際巨量資料探勘的程式設計以及機器學習等工具的應用方式。
Course Schedule	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)
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, 2nd edition, Cambridge University Press, 2014. References: 1. Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011. 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
Note