課程
| 學年/學期 Academic Year/Semester
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100 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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| 課程目標 Course objectives
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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.
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| 課程大綱 Course Description
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Part I: Web Search
• Evaluation
• Retrieval Model
• Language Model
• Link Analysis
• Web Crawling
Part II: Web Mining
• Classification
• Clustering
• Learning to Rank
Part III: Data-Intensive Information Processing
• Introduction to MapReduce
• MapReduce: the Programming Environment
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| 教學方式 Teaching approach
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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).
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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. (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
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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:
• Class participation: 10%
• Assignments: 30%
• Midterm exam: 30%
• Project: 30%
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| 教學助理基本資料 Teaching assistant tasks
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Grade assignments; Prepare assignments; Answer 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:
25% (4/16)
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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.了解當代主題意識與具有國際視野
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G.具有專業及道德責任的認知
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H.具有藝術涵養
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I.能接受多元文化與培養人文關懷之精神
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J.擁有公民素養並能履行公民責任
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