雲端運算與巨量資料分析
Cloud Computing and Big Data Analytics
| 節 | 週三 |
|---|---|
2 09:00–09:50 | 雲端運算與巨量資料分析 EE102（光復） 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course targets the computing resource allocation problem, and discusses how cloud computing solves the problem. In our discussion, the architecture, design, and management of modern cloud ecosystems, along with the key components in the ecosystems, will be covered. Furthermore, we will introduce important data analytics algorithms and tools, such as various data mining and machine learning algorithms. Afterward, we will make use of these tools to analyze realistic data on popular cloud computing platforms. At the end of the course, you will be able to: - Establish the basic concepts, key components, and impacting technologies in modern cloud ecosystems. - Establish a knowledge base of modern data mining and machine learning algorithms. - Acquire the experiences of leveraging modern data mining and machine learning algorithms in your own cloud applications. - Get familiar with the basic concepts about natural language processing and computer vision. - Acquire the experiences of processing big data on the cloud in real-world scenarios. - Use modern cloud ecosystems and tools for building your own cloud applications.
Computer Network, Computer Programming, Data Mining, Machine Learning
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- In-class participation (quizzes and in-class discussion) (20%) - Homework assignments (30%) - Midterm examination (20%) - Final project and demo (30%)
Cloud computing essentials
-Motivations of cloud computing -Cloud computing architecture: public, private, and hybrid -Cloud service models: anything as a service -Key technologies in cloud computing
- 講授：
- 9
- 示範：
- 3
Basics in data analytics
-Data mining algorithms: frequent-pattern mining, high-utility pattern mining, etc. -Machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning, federated learning, etc. -Data visualization techniques and tools
- 講授：
- 9
- 示範：
- 3
Key technologies, applications, and platforms of cloud computing
-Cloud resource management: load balancer, etc. -Recommendation systems -Data fabrics -Security & privacy of cloud computing -Virtualization technologies -Case study: Google Cloud Platform (GCP) -Case study: AWS Cloud Computing (EC2)
- 講授：
- 9
- 示範：
- 3
Parallel & distributed processing and data analytics
-Parallel computing models. -Hadoop and MapReduce. -Spark and PySpark. -Virtualization and containerization: Docker/rkt, Kubernetes, Scala, Erlang, and Elixir.
- 講授：
- 9
- 示範：
- 3
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to cloud computing 2025-02-19(三) |
| 第 2 週 | Building blocks of cloud ecosystems 2025-02-26(三) |
| 第 3 週 | Distributed & parallel processing: theories (I) 2025-03-05(三) |
| 第 4 週 | Distributed & parallel processing: theories (II) 2025-03-12(三) |
| 第 5 週 | Cloud security and privacy (I) 2025-03-19(三) |
| 第 6 週 | Cloud security and privacy (II) 2025-03-26(三) |
| 第 7 週 | Data analytics: data visualization (I) 2025-04-02(三) |
| 第 8 週 | Data analytics: data visualization (II) 2025-04-09(三) |
| 第 9 週 | Midterm examination 2025-04-16(三) |
| 第 10 週 | Data mining algorithms basics (I) 2025-04-23(三) |
| 第 11 週 | Data mining algorithms basics (II) 2025-04-30(三) |
| 第 12 週 | Machine learning algorithms basics (I) 2025-05-07(三) |
| 第 13 週 | Machine learning algorithms basics (II) 2025-05-14(三) |
| 第 14 週 | Case studies of cloud ecosystems (I) 2025-05-21(三) |
| 第 15 週 | Case studies of cloud ecosystems (II) 2025-05-28(三) |
| 第 16 週 | Final project live demo 2025-06-04(三) |
- Chellammal Surianarayanan and Pethuru Raj Chelliah, Essentials of Cloud Computing: A Holistic, Cloud-Native Perspective, Available at: https://link.springer.com/book/10.1007/978-3-031-32044-6, 2023. - Jiawei Han et al., Data Mining. Concepts and Techniques, 3rd Edition, Morgan Kaufmann, 2012. - Other supplement materials such as reference books and research papers might also be used.
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