教學大綱與進度

課程基本資料：

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
115-1	366624	終端人工智慧之實體技術與應用	1	3.0	3	★	洪子晏 范書愷	工管三甲 工管三乙	40	0	工管三甲乙合開

教學大綱與進度：

教師姓名	洪子晏
Email	georgehong618@gmail.com
最後更新時間	2026-08-18 08:23:55
課程大綱	本課程介紹終端人工智慧之實體技術與應用的核心概念與實務應用，聚焦於智慧系統如何透過感知、決策與行動與實體世界互動。課程內容涵蓋實體 AI 系統的重要基礎技術，包括機器學習感知模型、神經網路訓練、強化學習、模擬環境，以及系統整合等主題。並透過實作練習理解終端人工智慧於機器人、智慧裝置、自主系統等實體場域之實際應用建立對新興智慧實體技術發展趨勢的整體認識。 This course introduces the core concepts and practical applications of PhysicalTechnology and Edge AI, focusing on how intelligent systems interact with the physical world through perception, decision-making, and action. The course covers fundamental technologies of embodied AI systems, including machinelearning-based perception models, neural network training, reinforcement learning, simulation environments, and system integration. Through hands-on exercises, students will gain an understanding of the practical applications of Edge AI in physical domains such as robotics, smart devices, and autonomous systems, while developing a broad perspective on emerging trends in intelligent physical technologies.
課程進度 (1-16週)	1. Introduction to Edge AI and the applications 2. Simulation Platforms and Isaac Lab 3. Supervised Learning for Perception 4. Neural Networks and Model Training 5. Visual Perception for Embodied AI 6. Reinforcement Learning Fundamentals 7. Q-Learning and Deep Q-Networks 8. Running and Modifying RL Tasks 9. Integrating Perception and Decision 10. Project Development and Presentation
彈性學習 (17-18週)	類別 ● 學生分組實作及討論(Student group practice and discussion) 內容 During the flexible learning week, students will conduct self-directed learning and team-based project development based on the topics covered in the Edge AI course. Students will apply knowledge of perception, neural networks, simulation, and reinforcement learning to improve their final projects. Through independent exploration, team discussion, and system integration, students will strengthen their understanding of Edge AI applications and develop practical problem-solving skills. 時數 (小時) 6 學習成果 Students will apply knowledge of perception, neural networks, simulation, and reinforcement learning to improve their final projects to strengthen their understanding of Edge AI applications and develop practical problem-solving skills. 評量比例 10%
評量方式與標準	Homework and practice 50% Term Project 50%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 Li, B., et al. Embedded Artificial Intelligence: Principles, Platforms and Practices. Springer, 2024. Sutton, R. S., & Barto, A. G. Reinforcement Learning: An Introduction, 2nd ed. MIT Press, 2018.
課程諮詢管道	email to tyhong@ntut.edu.tw
延伸教學與資源	● 無 (None)
課程對應SDGs指標	SDG12：負責任的消費與生產（Responsible Consumption and Production）
課程是否導入AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
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