學年學期 Academic Year / Semester | 105學年度第2學期 | Spring Semester, 2017 | ||||
科目代號 Course Code | 753863001 | |||||
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 | ||||
選課人數 Number Registered | 0人 | |||||
學分數 No. of Credits | 3.0 | |||||
修別 Type of Credit | 選修 | Elective | ||||
先修科目 Prerequisite(s) | ||||||
上課時間 Session | 二EFG | tue18-21 | ||||
教室 Location | 研究250204 | 250204 Research Building(250204) | ||||
點閱核心能力分析圖與授課方式比例圖 |
This course includes the following two parts:
Part I: Web Search
• Evaluation
• Retrieval Model
• Language Model
• Link Analysis
• Web Crawling
Part II: Web Mining
• Classification
• Clustering
• Learning to Rank
• Recommendation
The purposes of this course includes the following points:
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;
4) to give students enough training for doing research in the field and an opportunity to work on a research project.
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. (2 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. (2 weeks) Web Mining 4 - Clustering: K-Means Clustering; Clustering and Search
10. (2 weeks) Web Mining 5 - Recommendation: Content-based approaches; Collaborative Filtering
每週課堂教學時數: 3 小時
每週預習/複習時數: 3 小時
Grading will be based on the following weighting scheme:
• Assignments: 25%
• Midterm Exam: 30%
• Projects: 45%
• Bonus (participation): <= 5%
Office Hours: Tue. 1-2pm or by email arrangement
Office Location: 大仁樓 413 研究室
陳志明-政大、中研院 TIGP 國際學程博士班二年級
He will help grade assignments, prepare assignments, and answer students' questions.
• 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.
需經教師同意始得使用