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	361551	Introduction to Artificial Intelligence and Internet of Things Applications	1	3.0	3	★	Meng-Yun Chung	4EE3A 4EE3B 4EE3C	25	0	電機大三合開,預選限25人

Course Plan：

Instructor	Meng-Yun Chung　Office Hours
Email	mychung@mail.ntut.edu.tw
Last Updated	2026-07-07 08:26:58
Course Objective	隨著人工智慧、5G通訊網路與感測技術的迅速發展，智慧物聯網應用已成為現代產業關注的核心領域。這些技術正加速整合，廣泛應用於智慧家庭、智慧醫療、工業自動化、軌道(台鐵、高鐵、捷運)系統及環境監測等多個場域，推動系統朝向智慧化、自動化與自我調適的方向發展。本課程旨在培養學生具備人工智慧與物聯網的基礎知識與實作能力，並透過專題與實驗導向的學習模式，深入理解智慧系統、資料處理的核心技術。課程內容涵蓋物聯網架構、感測器整合、資料傳輸模組、場域應用(軌道機電整合、軌道系統應用)、等主題，學生將學習如何使用Arduino等嵌入式平台設計與建置實體系統。除了傳統人工智慧的應用外，本課程亦有生成式人工智慧的潛在應用。學生將了解大型語言模型或圖像生成模型，進行資料合成、異常偵測、自然語言介面設計、影像識別以及智慧決策支援…等簡介，拓展物聯網應用的智能層次。為使學生具備設計與實作智慧物聯網系統的能力，並能將生成式AI融入創新應用中，為未來跨領域的技術整合與產業發展打下堅實基礎。 With the rapid development of artificial intelligence (AI), 5G communication networks, and sensing technologies, smart Internet of Things (IoT) applications have become a key focus in modern industries. These technologies are increasingly being integrated and widely applied in areas such as smart homes, healthcare, industrial automation, railway systems (Taiwan Railways, High Speed Rail, MRT) and environmental monitoring, driving systems toward greater intelligence, automation, and adaptability. This course is designed to equip students with fundamental knowledge and practical skills in both AI and IoT, adopting a project-based and hands-on learning approach to help them gain a deep understanding of core technologies in intelligent systems and data analysis. Course topics include IoT architecture, sensor integration, data transmission modules and field applications (railway electromechanical integration, rail system applications). Students will learn how to design and build physical systems using embedded platforms such as Arduino. In addition to conventional AI applications, the course also explores the potential of Generative Artificial Intelligence (Generative AI). Students will be introduced to large language models and image generation models, and how they can be applied in data synthesis, anomaly detection, natural language interface design, image recognition and intelligent decision support, further enhancing the intelligence level of IoT applications. The ultimate goal of this course is to enable students to design and implement smart IoT systems and to integrate generative AI into innovative applications, laying a solid foundation for future interdisciplinary technology integration and industry development.
Course Schedule	第1週：課程準備週第2週：實驗室安全守則教育訓練 (小考) 第3週：物聯網技術簡介第4週：物聯網技術簡介(硬體篇) 第5週：物聯網技術簡介(硬體篇) 第6週：物聯網技術簡介(軟體篇) 第7週：物聯網技術簡介(軟體篇) 第8週：物聯網技術應用與設計(軌道領域實例運用…等) 第9週：主題簡報1 第10週：主題簡報1 第11週：人工智慧導論(基礎篇) 第12週：人工智慧導論(進階篇) 第13週：生成式人工智慧簡介第14週：生成式人工智慧應用第15週：人工智慧物聯網之道德倫理第16週：期末考 (筆試) 第17週：主題簡報2 第18週：主題簡報2 課程將依據授課情況修改。 Week 1 ： Introduction Week 2 ： Laboratory Safety and Health Training Course (Quiz) Week 3 ： Introduction of IoT Technology Week 4 ： Introduction of IoT Technology (Hardware) Week 5 ： Introduction of IoT Technology (Hardware) Week 6 ： Introduction of IoT Technology (Software) Week 7 ： Introduction of IoT Technology (Software) Week 8 ： Application of IoT Technology (Examples in the rail field…etc) Week 9 ： Theme report I Week 10 ： Theme report I Week 11 ： Introduction to Artificial Intelligence (Fundamental) Week 12 ： Introduction to Artificial Intelligence (Advanced) Week 13 ： Introduction to Gen AI Week 14 ： Application of Gen AI Week 15 ： Ethics and Moral Considerations in AIoT Week 16 ： Final exam (Written) Week 17 ： Theme report II Week 18 ： Theme report II The change of course schedule will be subject to a real time-limit.
Evaluation and grading policy	期中考：0% 期末考：10% 主題報告：40%(期中、期末各佔20%) 實驗成績：40% 平時成績：10% Mid-term exam: 0% Final exam: 10% Theme report: 40% Experiment Results: 40% Attendance: 10%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 使用自編教材 / Use of self-made materials
The access to curricular consultation	以email或line聯絡，預約課程後時間輔導。 E-mail: mychung@mail.ntut.edu.tw Book the After-school tutoring by email or line. Email: mychung@mail.ntut.edu.tw
Expanding teaching and resources	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment. ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） 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	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note	17至18週彈性學習內容：學生分組實作及討論。以錄影報告方式進行(檔案上傳至指定地方，無需現場上課)，並且每組彼此評分、寫相關問題解答與心得。每組需要在其中安排至少2個問題給其他組進行解答。佔總成績20% Flexible learning content for weeks 17-18: Students will engage in group practice and discussions. This will be conducted in theme report, with each group evaluating each other, writing answers to related questions, and sharing their reflections. The theme report need to upload to the cloud and don`t need to come to site. Each group must include at least two questions for other groups to answer. The Grading is 20%.