| 課程與核心能力關聯配比(%) |
課程目標之教學方法與評量方法 |
| 課程目標 |
核心能力 |
配比(%) |
教學方法 |
評量方法 |
| This course intends to introduce the concepts of Artificial Intelligence (AI) and its implementations. Also, each student should learn how to code and apply the algorithms so as to analyze the real-world problems. |
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| 授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習) |
| 週次 |
授課內容 |
| 第1週 |
Introduction to Artificial Intelligence (AI) |
| 第2週 |
Applications of AI and Its Problem Solving |
| 第3週 |
The Introduction of AI Procedure - I |
| 第4週 |
The Introduction of AI Procedure - II |
| 第5週 |
The Types of Machine Learning |
| 第6週 |
Machine Learning - Classification |
| 第7週 |
Machine Learning - Regression |
| 第8週 |
Project Proposal |
| 第9週 |
Machine Learning - Summary |
| 第10週 |
Introduction to Neural Network (NN) - I |
| 第11週 |
Introduction to Neural Network (NN) - II |
| 第12週 |
Implementation - I |
| 第13週 |
Implementation - II |
| 第14週 |
Implementation - III |
| 第15週 |
Data Visualization |
| 第16週 |
Final Presentation
Self-directed Learning I - Participate in relevant lectures, workshops, or seminars using materials designated by the instructors.
Self-directed Learning II - Participate in relevant lectures, workshops, or seminars using materials designated by the instructors. |
自主學習
內容 |
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| 學習評量方式 |
| Homework/Participation (40%), Project Proposal (20%), Final Presentation (30%), Self-directed Learning Report (10%) |
| 教科書＆參考書目(書名、作者、書局、代理商、說明) |
Main Textbook: Handout
● Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) Foundations of Machine Learning (2/e), MIT Press, ISBN: 9780262039406.
● Albon, Chris (2018) Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning, O’Reilly, ISBN: 9781491989388.
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| 課程教材（教師個人網址請列在本校內之網址） |
| The course materials would be found at ilearn. |
| 課程輔導時間 |
| Wednesday 10:00-11:00 am or please contact the teacher to arrange a meeting time. |
| 聯合國全球永續發展目標(連結網址) |
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