Type of Credit: Elective
Credit(s)
Number of Students
This course is designed to teach students the following points:
能力項目說明
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
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
| 週次 | 課程主題 | 課程內容與指定閱讀 | 教學活動與作業 | 學習投入時數 | |
|---|---|---|---|---|---|
| 課堂講授 | 課程前後 | ||||
|
1 |
Course Introduction
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
2 |
Introduction to Web Search and Mining
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
3 |
Web Search: Ranking Evaluation
|
Slides
|
Textbook; Assignment
|
3.0 |
4.5 |
|
4 |
Web Search: Vector Space Model
|
Slides
|
Textbook; Project
|
3.0 |
4.5 |
|
5 |
Web Search: Probabilistic Information Retrieval
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
6 |
Web Search: Language Model for IR
|
Slides
|
Textbook; Assignment
|
3.0 |
4.5 |
|
7 |
Web Search: Text Analytics
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
8 |
清明節放假
|
none
|
none
|
none |
none |
|
9 |
Midterm
|
None
|
None
|
0 |
1.5 |
|
10 |
Web Mining: Introduction to Machine Learning Techniques
|
Slides
|
Textbook; Project
|
3.0 |
4.5 |
|
11 |
Web Mining: Classification and Naive Bayes
|
Slides
|
Textbook; Assignmnet
|
3.0 |
4.5 |
|
12 |
Web Mining: Support Vector Machines (I)
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
13 |
Web Mining: Support Vector Machines (II)
|
Slides
|
Textbook; Project
|
3.0 |
4.5 |
|
14 |
Web Mining: Clustering (I)
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
15 |
Web Mining: Clustering (II)
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
16 |
Web Mining: Recommender Systems (I)
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
17 |
Web Mining: Recommender Systems (II)
|
Slides
|
Textbook
|
3.0 |
4.5 |
|
18 |
Final Project Presenations
|
None
|
None
|
0 |
1.5 |
Grading will be based on the following weighting scheme:
• Assignments: 25%
• Midterm Exam: 30%
• Projects: 45%
• Bonus (participation): <= 5%
• 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.