Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366624	Physical Technology and Application of Edge AI	1	3.0	3	★	Hong,Tzu-Yen Shu-Kai Fan	4IEM3A 4IEM3B	34	0	工管三甲乙合開

Course Plan：

Instructor	Hong,Tzu-Yen
Email	georgehong618@gmail.com
Last Updated	2026-08-18 08:23:55
Course Objective	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.
Course Schedule (Week 1-16)	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
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) Content 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. Hours 6 Learning Outcomes 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. Assessment Percentage 10%
Evaluation and grading policy	Homework and practice 50% Term Project 50%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 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.
The access to curricular consultation	email to tyhong@ntut.edu.tw
Expanding teaching and resources	● 無 (None)
The course corresponds to the SDGs	SDG12：負責任的消費與生產（Responsible Consumption and Production）
Does the course incorporate 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）
Note