| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| 使學生初步學習利用人工智慧與深度學習模式解決環境問題，以作為未來研究或工作基礎 |
| 1.Expertise in environmental engineering. |
| 2.Ability to plan and execute research on environmental engineering topics. |
| 4.Innovative thinking and independent problem-solving skills. |
| 5.Ability to coordinate and integrate with people from different fields. |
|
|
| topic Discussion / Production |
| Discussion |
| Lecturing |
|
| Written Presentation |
| Attendance |
| Quiz |
| Internship |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
第1週. 課程簡介:認識AI人工智慧
Course Introduction: AI artificial intelligence |
| Week 2 |
第2週. AI導論1
Artificial intelligence introduction I |
| Week 3 |
第3週. 基本的神經網路架構 I
Neuro network introduction I |
| Week 4 |
第4週. 基本的神經網路架構 II
Neuro network introduction II |
| Week 5 |
第5週. 個人文獻閱讀報告: 個人報告 |
| Week 6 |
第6週. 個人文獻閱讀報告: 個人報告 |
| Week 7 |
第7週. AI人工智慧與基礎影像處理介紹I
AI and Introduction to basic image processing I |
| Week 8 |
第8週. AI人工智慧與基礎影像處理介紹I
AI and Introduction to basic image processing I |
| Week 9 |
第9週. AI人工智慧與環境管理應用 I
Artificial Intelligence and Environmental Management I |
| Week 10 |
第10週. AI人工智慧與環境管理應用 II
Artificial Intelligence and Environmental Management II |
| Week 11 |
第11週. AI人工智慧與環境管理應用 I
Artificial Intelligence and Environmental Management I |
| Week 12 |
第12週. AI人工智慧與環境管理應用 II
Artificial Intelligence and Environmental Management II |
| Week 13 |
第13週. 期中報告: 個人報告 |
| Week 14 |
第14週. 期中報告: 個人報告 |
| Week 15 |
第15週. Windows安裝TensorFlow與Keras 1. 安裝Anaconda教學 2. 命令提示字元操作環境介紹 3. 建立TensorFlow 5. 啟動spyder
Install Anaconda, TensorFlow and Keras on the windows system 1. Introduction to command prompt 2. Establish Anaconda’s virtual environment 3. Install TensorFlow and Keras 4. Start spyder |
| Week 16 |
第16週. 卷積網路CNN模式於影像處理與辨識之實作
Implementation of CNN mode in Image Processing and Recognition
第17週. RNN模式於環境數據分析之實作
Implementation of RNN Mode in environment data and analysis
第18週. 團隊實作報告
Group Presentation |
self-directed
learning |
|
|
| Evaluation |
| 1或2次報告佔30%、團隊實作書面報告佔30%、出席等學習狀況佔10% |
| Textbook & other References |
1. 人工智慧導論，鴻海教育基金會，2019。
2. 深度學習的16堂課，旗標， 2021 08.
3. Python機器學習與深度學習特訓班，基峰，2019 02.
4. 自編講義
|
| Teaching Aids & Teacher's Website |
| 部分上課講義或投影片上傳至學生iLearning 系統供學生下載參閱 |
| Office Hours |
| 每週四上午九點至十二點為 Office Hour |