NCHU Course Outline
Course Name (中) 機器學習物種空間型態分析與模擬(4006)
(Eng.) Machine Learning for Spatial Pattern Analysis and Simulation in Species
Offering Dept Department of Forestry (Forest Science Program)
Course Type Elective Credits 3 Teacher HSU YU-CHUN
Department Department of Forestry (Forest Science Program)/Undergraduate Language English Semester 2026-SPRING
Course Description This course introduces artificial intelligence from a conceptual and interdisciplinary perspective, focusing on its applications in species spatial pattern analysis and forest monitoring. The course begins with the historical development and philosophical foundations of AI, followed by an introduction to fundamental concepts in machine learning and deep learning.

The course emphasizes real-world applications in plant monitoring, forest ecosystem assessment, biodiversity analysis, and environmental sustainability. In addition, ethical considerations, emotional dimensions of human–AI interaction, and future technological trajectories are critically examined. Through case studies and discussion-based learning, students will develop literacy in AI technologies and understand their implications for ecological research and forest management.

Upon completion, students will gain foundational knowledge of artificial intelligence concepts and their applications in forest science and species spatial analysis.
Prerequisites
self-directed learning in the course Y
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
1.Understand the historical evolution and foundational concepts of artificial intelligence.
2.Explain basic principles of machine learning and deep learning in non-technical terms.
3.Recognize applications of AI in plant monitoring and forest ecosystem analysis.
4.Evaluate future technological developments and their potential impact on forestry and environmental sustainability.
5.Develop interdisciplinary thinking linking AI, ecology, and sustainability.
1.Basic knowledge and skills in forestry
2.Theories and practices in forest biology, conservation and ecology
4.Theories and practices in forest management
10
10
80
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Introduction to Artificial Intelligence: History, Myths, and Reality
Week 2 Introduction to Artificial Intelligence: History, Myths, and Reality
Week 3 The Evolution of AI
Week 4 Machine Learning Basic Concepts and Intuition
Week 5 Machine Learning Basic Concepts and Intuition
Week 6 AI in Plant Monitoring and Forestry Applications
Week 7 Midterm Examination
Week 8 Midterm Presentation
Week 9 AI in Forest Monitoring and Biodiversity Analysis
Week 10 Species Spatial Patterns
Week 11 GeoAI
Week 12 GeoAI
Week 13 Ethics of Artificial Intelligence
Week 14 Emotion, Trust, Decision-Making, and Smart Forests
Week 15 Final Project Presentation
Week 16 Final Examination
self-directed
learning		    03.Preparing presentations or reports related to industry and academia.
Evaluation
Performance: 30%
Midterm exam and report: 30%
Final exam and report: 40%
Textbook & other References
1. 三津村直貴. (2023). 圖解AI人工智慧 [図解まるわかりAIのしくみ]. 溫政堯 (譯). 碁峰資訊股份有限公司.
2. 石田保輝, & 宮崎修一. (2017). アルゴリズム図鑑: 絵で見てわかる26のアルゴリズム. 陳彩華 (譯). 臉譜出版社.
3. 高橋海渡, 立川裕之, 小西功記, 小林寬子, & 石井大輔. (2023). 完全圖解人工智慧: 零基礎也OK! 從NLP、圖像辨識到生成模型, 現代人必修的53堂AI課 [図解即戦力: AIのしくみと活用がこれ1冊でしっかりわかる教科書]. 陳識中 (譯). 台灣東販股份有限公司.
Teaching Aids & Teacher's Website

Office Hours
Wednesday and Friday 12:00
Sustainable Development Goals, SDGs(Link URL)
15.Life On Landinclude experience courses：N
Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day：2026/02/18 14:29:13 Printed Date, year/month/day：2026 / 3 / 30
The second-hand book website：http://www.myub.com.tw/