推薦系統
Introduction to Recommender Systems
學期
112-2
學分
3
學分
當期課號
555008
永久課號
EEDE30056
開課單位
電機學院碩士在職專班
授課教師
黃俊龍
校區
光復
類別
選修
上課時間表
| 節 | 週四 |
|---|---|
A 18:30–19:20 | 推薦系統 EC016 3 節連堂 |
B 19:30–20:20 | |
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
概述
In the first part, I will introduce several fundamental machine learning algorithms for prediction. In the second part, several recommendation algorithms such as collaborative filtering will be introduced.
課程大綱
- Data Insights to Decisions
- Data Exploration
- Information-based Learning
- Similarity-based Learning
- Probability-based Learning
- Error-based Learning
- Evaluation
- An Introduction to Recommender Systems
- Neighborhood-based Collaborative Filtering
- Model-based Collaborative Filtering
- Content-based Recommender Systems
- Knowledge-based Recommender Systems (Optional)
- Ensemble-based and Hybrid Recommender Systems (Optional)
- Evaluating Recommender Systems
- Time- and Location-Sensitive Recommender Systems (Optional)
週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | |
| 第 2 週 | |
| 第 3 週 | |
| 第 4 週 | |
| 第 5 週 | |
| 第 6 週 | |
| 第 7 週 | |
| 第 8 週 | |
| 第 9 週 | |
| 第 10 週 | |
| 第 11 週 | |
| 第 12 週 | |
| 第 13 週 | |
| 第 14 週 | |
| 第 15 週 | |
| 第 16 週 |
教科書
John D. Kelleher , Brian MAC Namee , Aoife D'arcy, Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, MIT Press. Charu C. Aggarwal, Recommender Systems: The Textbook