巨量資料分析技術與應用
Big Data Analytics Techniques and Applications
| 節 | 週三 |
|---|---|
3 10:10–11:00 | 巨量資料分析技術與應用 ED301（光復） 2 節連堂 |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course will be conducted in English and will introduce the fundamental concepts and modern techniques of big data analysis. Topics include classic algorithms, graph-enhanced models, generative models, and discussions of real-world applications. Assignments, paper discussions, and project implementation are also included to help build foundational knowledge.
Machine Learning, Data Mining, Data Structure, Python Programming
無備註
The course is mainly taught using lecture slides, with assignments and a project for hands-on practice. Students will also read and present research papers to better understand model design thinking.
3-4 assignments、1 final project、1 paper presentation & discussion Assignments：40% Final Project：35% Paper Presentation & Discussion：25%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course Introduction and Overview of Big Data |
| 第 2 週 | Basic Techniques for Big Data Analysis (1/2) |
| 第 3 週 | Basic Techniques for Big Data Analysis (2/2) |
| 第 4 週 | Relational Data Modeling |
| 第 5 週 | Data Stream Mining |
| 第 6 週 | Community Detection |
| 第 7 週 | Graph Representation Learning |
| 第 8 週 | Paper Presentation & Discussion (1/2) |
| 第 9 週 | Paper Presentation & Discussion (2/2) |
| 第 10 週 | Knowledge Graph Analysis |
| 第 11 週 | Knowledge Graph Analysis (2/2) |
| 第 12 週 | Graph-Based Reasoning |
| 第 13 週 | Recommender System |
| 第 14 週 | Large Generative Models |
| 第 15 週 | Advanced Topics |
| 第 16 週 | Project Presentation & Demo Session |
Leskovec, Jure, Anand Rajaraman, Jeffrey David Ullman. Mining of massive data sets. Cambridge University Press, 2020. ISBN：9781108476348
- 地點
- 教師未提供此項資料
- 時間
- By appointment
- 聯絡方式
- By email: lpyting@nycu.edu.tw