學年學期 Academic Year / Semester 101學年度第2學期 Spring Semester, 2013
開課單位 Course Department 資科三、資科四、資科碩一、資科碩二 MA Program of Computer Science, Second Year
課程名稱 Course Name (中 Ch.)網路搜索與探勘 (英 Eng.)Web Search and Mining
授課教師 Instructor 蔡銘峰 TSAI MING-FENG
職稱 Title 專任助理教授 Assistant Professor
學分數 No. of Credits 3.0
修別 Type of Credit 選修 Elective
先修科目 Prerequisite(s)
點閱核心能力分析圖與授課方式比例圖
課程目標 Course objectives

The goal of this course is: 1) to provide an overview of Web Search and Mining related research, 2) to systematically review the core research topics in the field, 3) to show case the most recent research progress, and 4) to give students enough training for doing research in the field and an opportunity to work on a research project.

課程大綱 Course Description

Part I: Web Search
• Evaluation
• Retrieval Model
• Language Model
• Link Analysis
• Web Crawling

Part II: Web Mining
• Classification
• Clustering
• Learning to Rank
• Recommendation

Part III: Data-Intensive Information Processing
• Introduction to MapReduce
• MapReduce: the Programming Environment

上課進度 Class schedule

1. (1 week) Introduction: Goals and history of Web Search and Mining; IR vs. Web Search; DM vs. Web Mining.
2. (2 weeks) Web Search 1 - Ranking Evaluation; Probabilistic Information Retrieval
3. (2 weeks) Web Search 2 - Language Model for Information Retrieval
4. (2 weeks) Web Search 3 - Processing Text: Text statistics; Link Analysis
5. (1 week) Web Search 4 - Web Crawling
6. (1 week) Web Mining 1 - Classification and Naive Bayes
7. (2 weeks) Web Mining 2 - Supported Vector Machines; K Nearest Neighbor
8. (2 weeks) Web Mining 3 - Clustering: Flat clustering and Hierarchical clustering
9. (1 weeks) Web Mining 4 - Clustering: K-Means Clustering; Clustering and Search
10. (2 weeks) Data-Intensive Information Processing - Overview of Cloud Computing; Map Reduce; Hadoop

教學方式 Teaching approach

The course will involve lectures by instructor, student presentations, and research projects on major research topics in Web Search and Mining related research. Students are expected to read quite a few research papers and present some of them at the class. There will be a midterm and a few assignments. Students are also required to finish a course project (group work is allowed and encouraged).

教學助理工作項目 Teaching assistant tasks

Grade assignments; Prepare assignments; Answer Questions

課程要求/評分標準 Course requirements/Grading standards

Grading will be based on the following weighting scheme:
• Class participation: 10%
• Assignments: 30%
• Midterm exam: 30%
• Project: 30%

參考書目 Textbook & references

• Introduction to Information Retrieval, by C. Manning, P. Raghavan, and H. Schütze.
• Search Engines: Information Retrieval in Practice, by Bruce Croft, Donald Metzler, Trevor Strohman.
• Data-Intensive Text Processing with MapReduce, by Jimmy Lin and Chris Dyer.
• Hadoop: The Definitive Guide, by Tom White.

課程相關連結 Course related links

本課程附件 Course attachments