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	367134	Intelligent Production Technology	1	3.0	3	★	LI, CHIH-HUNG	GME GMT	55	0

Course Plan：

Instructor	LI, CHIH-HUNG　Office Hours
Email	cl4e@mail.ntut.edu.tw
Last Updated	2026-09-07 00:01:56
Course Objective	本課程以智能化生產為核心，介紹人工智慧、資料分析與工業資訊技術的基本原理及其在製造領域的應用。課程內容涵蓋人工智慧發展、機器學習模型、資訊理論、人工神經網路、深度學習與卷積神經網路、大型語言模型、檢索增強生成（RAG）及強化學習，並延伸至工業物聯網、雲端服務、大數據與管理系統。透過異常檢測、智慧維護及虛擬量測等專題，探討相關技術如何支援生產管理與製程改善。課程亦結合氛圍編程（Vibe Coding）專題實作與報告，引導學生運用自然語言與生成式人工智慧輔助程式開發，培養整合所學、驗證成果及解決智能化生產問題的能力。 This course focuses on intelligent production, introducing the fundamental principles of artificial intelligence, data analysis, and industrial information technologies, as well as their applications in manufacturing. Topics include the development of artificial intelligence, machine learning models, information theory, artificial neural networks, deep learning and convolutional neural networks, Large Language Models, Retrieval-Augmented Generation (RAG), and reinforcement learning. The course also covers the Industrial Internet of Things (IIoT), cloud services, big data, and management systems. Through special topics on anomaly detection, intelligent maintenance, and virtual metrology, students explore how these technologies support production management and process improvement. The course also incorporates Vibe Coding project work and presentations, guiding students in using natural language and generative artificial intelligence to assist with software development while cultivating their ability to integrate acquired knowledge, validate results, and solve problems in intelligent production.
Course Schedule (Week 1-16)	週次 單元主題 第一週 智能化生產技術簡介 第二週 人工智慧的過去與現在/機器學習。 第三週 機器學習模型/資訊理論。 第四週 人工神經網路 第五週 深度學習/卷積神經網路/大型語言模型 第六週 強化學習 第七週 工業物聯網/雲端服務/大數據 第八週 期中考 第九週 管理系統 第十週 異常檢測特論 第十一週 智慧維護特論 第十二週 虛擬量測特論 第十三週 氛圍編程(vibe coding)專題報告I 第十四週 氛圍編程(vibe coding)專題報告II 第十五週 氛圍編程(vibe coding)專題報告III 第十六週 氛圍編程(vibe coding)專題報告IV 第十七週 彈性學習 第十八週 彈性學習 Week 1 Introduction to Intelligent Production Technology Week 2 The Past and Present of Artificial Intelligence/Machine Learning Week 3 Machine Learning models/Information Theory Week 4 Artificial Neural Networks Week 5 Deep Learning/CNN/LLM Week 6 Reinforcement Learning Week 7 IIoT/Cloud/Big Data Week 8 Mid-term examination Week 9 Management Systems Week 10 Special topic: Anomaly Detection Week 11 Special topic: Intelligent Maintenance Week 12 Special topic: Virtual Metrology Week 13 Vibe coding report I Week 14 Vibe coding report II Week 15 Vibe coding report III Week 16 Vibe coding report IV Week 17 Flexible Learning Week 18 Flexible Learning
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) Content 彈性學習週期間，各組同學須針對第十三至十六週已完成口頭報告的氛圍編程（Vibe Coding）專題，彙整成果並繳交一份書面報告，說明資料來源、分析目標、研究方法、實作流程及結果討論。 During the flexible learning weeks, each team must compile the results of its Vibe Coding project presented orally during Weeks 13–16 and submit a written report covering the data sources, analytical objectives, methodology, implementation process, and results and discussion. Hours 6 Learning Outcomes 繳交書面報告。 Submit a written report. Assessment Percentage 10%
Evaluation and grading policy	作業: 20% 期中考: 40% 專題口頭報告: 30% 彈性學習: 10% Homework: 20% Mid-term examination: 40% Project oral report: 30% Flexible Learning: 10%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 工業4.0 智能化生產技術與實作 李志鴻著 參考書目: Machine Learning/Tom M. Mitchell/McGraw-Hill Deep Learning/Ian Goodfellow, Yoshua Bengio, Aaron Courville/MIT Press (http://www.deeplearningbook.org) Textbook: Industry 4.0 - Intelligent Production Technology and Practice, Chih-Hung Li (Teacher's notes) Reference Books: Machine Learning/Tom M. Mitchell/McGraw-Hill Deep Learning/Ian Goodfellow, Yoshua Bengio, Aaron Courville/MIT Press (http://www.deeplearningbook.org)
The access to curricular consultation	綜合科館714-2室 Place: Room 714-2, Integrated Technology Complex
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. ● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） 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） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	無 None