課程
| 學年/學期 Academic Year/Semester
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105 學年 第 2 學期
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課程名稱 Course Name
753863-001
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(中 Ch.)網路搜索與探勘
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(英 Eng.)Web Search and Mining
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| 授課教師 Instructor
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蔡銘峰
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| 修別 Type of Credit
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選修
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學分數No. of Credits
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3.0
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| 備註 Note
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N/A
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| 課程簡介 Course Description
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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
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| 課程目標與學習成效 Course Objectives & Learning Outcomes
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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.
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| 每週課程進度與作業要求【請詳述每週課程內容／授課方式與學生預習內容／學習活動／課後作業】
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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
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| 評量工具與策略、評分標準 Evaluation Criteria
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Grading will be based on the following weighting scheme:
• Assignments: 25%
• Midterm Exam: 30%
• Projects: 45%
• Bonus (participation): <= 5%
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| 授課教師 Office Hours、地點 Office Location
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Office Hours: Tue. 1-2pm or by email arrangement
Office Location: 大仁樓 413 研究室
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| 教學助理基本資料 Teaching assistant tasks
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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.
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指定/參考書目 Textbook & references
（為維護智慧財產權，請務必使用正版書籍）
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• 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.
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| 課程相關連結 Course related links
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N/A
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| 本課程附件 Course attachments
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N/A
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| 課程進行中，是否禁止使用智慧型手機、平板等隨身設備。
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需經教師同意始得使用
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學生自評核心能力填答率:
18.18% (10/55)
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能力項目說明：
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A.培養邏輯推理、獨立思考與創新能力
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B.理解自然科學與數位科技
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C.培養團隊合作的能力
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D.具備有效的溝通表達能力
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E.養成終身學習與自我提升能力
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F.瞭解資訊科技發展趨勢與具備國際視野
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G.具有專業及道德責任的認知
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