Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2025 345082 AI Applications in Environmental Engineering and Management 1 3.0 3 ★ Lin Wen-Yinn GEEM 6 0 億光大樓1343大教室

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	345082	AI Applications in Environmental Engineering and Management	1	3.0	3	★	Lin Wen-Yinn	GEEM	6	0	億光大樓1343大教室

Course Plan：

Instructor Lin Wen-Yinn　Office Hours

Email wylin@ntut.edu.tw

Last Updated 2024-12-19 17:07:23

Course Objective 人工智慧工具可以提供環境工程與管理領域諸多助益，使用人工智慧工具的主要優勢之一是能夠有效地分析和處理大量數據。環境數據的複雜性是一個具有挑戰性的課題，而人工智慧提供了實現更精確預測、持續監控、風險評估與問題解決方案。本課程將簡要介紹人工智慧技術的發展，以及人工智慧相關物聯網、環境監測、環境品質預測、環境研究等領域應用事例，還有人工智慧工具使用對環境可能的衝擊影響。人工智慧工具可以提供環境工程與管理領域諸多助益，使用人工智慧工具的主要優勢之一是能夠有效地分析和處理大量數據。環境數據的複雜性是一個具有挑戰性的課題，而人工智慧提供了實現更精確預測、持續監控、風險評估與問題解決方案。本課程將簡要介紹人工智慧技術的發展，以及人工智慧相關物聯網、環境監測、環境品質預測、環境研究等領域應用事例，還有人工智慧工具使用對環境可能的衝擊影響。 AI tools can provide many benefits in the field of environmental engineering and management, and one of the main advantages of using AI tools is their ability to effectively analyze and process large amounts of data. The complexity of environmental data is a challenging obstacle, and AI provides more accurate predictions, continuous monitoring, risk assessment, and problem solving. This course will briefly introduce the development of AI technology, as well as application cases in the fields of AI-related Internet of Things, environmental monitoring, environmental quality prediction, environmental research, etc., as well as

Course Schedule 1. Introduction 2. 機器學習概念與介紹 3. Python語言基礎學習I 4. Python語言基礎學習II 5. Python語言進行機器學習應用I 6. Python語言進行機器學習應用I 7. History and Current Status of Artificial Intelligence Development 8. Ethics and Morality of AI Development 9. Deep Learning and the Foundations of Natural Language 10. Midterm Report 11. Characteristics and Challenges of Environmental Data 12. Pre-processing of Environmental Data 13. Statistical Analysis and Visualization 14. Spatial and Temporal Data Analysis 15. Environmental Sensors and Internet of Things (IoT) 16. Pollution Monitoring, Prediction, and Pollution Source Tracking 17. AI Applications in Resource Recovery 18. Final Report

Evaluation and grading policy 1. Class Attendance and Participation 30% 2. Midterm Report 30% 3. Final Report 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
1. Artificial Intelligence: A Modern Approach 4/E (Stuart Russell and Peter Norvig) 2. Artificial Intelligence Methods in the Environmental Sciences (Sue Ellen Haupt, Antonello Pasini and Caren Marzban)

The access to curricular consultation Please email Professor Lin (wylin@ntut.edu.tw) to discuss or arrange meeting.

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG6：潔淨水與衛生（Clean Water and Sanitation）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）

Does the course incorporate AI

Note
　

Instructor	Lin Wen-Yinn　Office Hours
Email	wylin@ntut.edu.tw
Last Updated	2024-12-19 17:07:23
Course Objective	人工智慧工具可以提供環境工程與管理領域諸多助益，使用人工智慧工具的主要優勢之一是能夠有效地分析和處理大量數據。環境數據的複雜性是一個具有挑戰性的課題，而人工智慧提供了實現更精確預測、持續監控、風險評估與問題解決方案。本課程將簡要介紹人工智慧技術的發展，以及人工智慧相關物聯網、環境監測、環境品質預測、環境研究等領域應用事例，還有人工智慧工具使用對環境可能的衝擊影響。人工智慧工具可以提供環境工程與管理領域諸多助益，使用人工智慧工具的主要優勢之一是能夠有效地分析和處理大量數據。環境數據的複雜性是一個具有挑戰性的課題，而人工智慧提供了實現更精確預測、持續監控、風險評估與問題解決方案。本課程將簡要介紹人工智慧技術的發展，以及人工智慧相關物聯網、環境監測、環境品質預測、環境研究等領域應用事例，還有人工智慧工具使用對環境可能的衝擊影響。 AI tools can provide many benefits in the field of environmental engineering and management, and one of the main advantages of using AI tools is their ability to effectively analyze and process large amounts of data. The complexity of environmental data is a challenging obstacle, and AI provides more accurate predictions, continuous monitoring, risk assessment, and problem solving. This course will briefly introduce the development of AI technology, as well as application cases in the fields of AI-related Internet of Things, environmental monitoring, environmental quality prediction, environmental research, etc., as well as
Course Schedule	1. Introduction 2. 機器學習概念與介紹 3. Python語言基礎學習I 4. Python語言基礎學習II 5. Python語言進行機器學習應用I 6. Python語言進行機器學習應用I 7. History and Current Status of Artificial Intelligence Development 8. Ethics and Morality of AI Development 9. Deep Learning and the Foundations of Natural Language 10. Midterm Report 11. Characteristics and Challenges of Environmental Data 12. Pre-processing of Environmental Data 13. Statistical Analysis and Visualization 14. Spatial and Temporal Data Analysis 15. Environmental Sensors and Internet of Things (IoT) 16. Pollution Monitoring, Prediction, and Pollution Source Tracking 17. AI Applications in Resource Recovery 18. Final Report
Evaluation and grading policy	1. Class Attendance and Participation 30% 2. Midterm Report 30% 3. Final Report 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 1. Artificial Intelligence: A Modern Approach 4/E (Stuart Russell and Peter Norvig) 2. Artificial Intelligence Methods in the Environmental Sciences (Sue Ellen Haupt, Antonello Pasini and Caren Marzban)
The access to curricular consultation	Please email Professor Lin (wylin@ntut.edu.tw) to discuss or arrange meeting.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG6：潔淨水與衛生（Clean Water and Sanitation） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
Does the course incorporate AI
Note