查詢教學大綱與進度

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

114-2 360377 人工智慧跨域專題實作 1 3.0 3 ★ 李文毅
鍾建屏管理學院(大) 29 0 總整課程

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
114-2	360377	人工智慧跨域專題實作	1	3.0	3	★	李文毅鍾建屏	管理學院(大)	29	0	總整課程

教學大綱與進度：

教師姓名李文毅

Email wy.lee@ntub.edu.tw

最後更新時間 2026-01-20 13:42:25

課程大綱本課程整合微軟Azure Open AI雲端服務資源，運用問題分析能力進行實作與相關應用，以企業出題學生解題PBL模式導入人工智慧跨域專題實作，目的在幫助學生瞭解如何運用人工智慧技術來解決現實世界中的問題。本課程透過導入人工智慧在企業管理和決策、金融、數據分析、智慧製造等領域等應用案例，並以通用人工智慧跨域應用學習模式來跨域應用至數據分析、智慧運算、智慧製造及物流、商情預測、經營管理、金融等領域。透過本課程的修習，學生將獲得實際的跨學科專題實作能力，掌握人工智慧技術的應用和發展趨勢，養成專業職能和研發實力，並培育具有創業與創新精神人才，了解產業在數位轉型中，所面臨的困難與挑戰。 This course integrates resources from Microsoft Azure's Open AI cloud services, utilizing problem analysis skills for practical implementation and related applications. It introduces an industry-proposed, student-solved Project-Based Learning (PBL) model into the interdisciplinary project implementation of artificial intelligence. The aim is to help students understand how to apply AI technology to solve real-world problems. Through the introduction of artificial intelligence applications in enterprise management and decision-making, finance, data analysis, and smart manufacturing, the course enables students to gain practical interdisciplinary project implementation skills. This course also helps students understand the difficulties and challenges faced by industries in digital transformation.

課程進度
(1-16週) Week 1. Introduction/The Fundamentals of Machine Learning Week 2. End-to-End Machine Learning Project/Classification Week 3. Training Models/Support Vector Machines Week 4. Decision Trees/ Ensemble Learning and Random Forests Week 5. Dimensionality Reduction/ Unsupervised Learning Techniques Week 6. Introduction to Artificial Neural Networks Week 7. Training Deep Neural Networks Week 8. Deep Neural Networks with PyTorch Week 9. Midterm Week 10. Deep learning related issues Week 11. Deep learning related issues Week 12. Azure Open AI Week 13. Azure Open AI Week 14. Azure Open AI Week 15. Azure Open AI Week 16. Azure Open AI Week 17. Azure Open AI Week 18. Final

評量方式與標準 20% Homework 30% Midterm 40% Final 20% Class participation

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
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd 出版者: O'Reilly Media; 第3版 (2022年 11月 8日) 語言: English Paperback: 861 頁 ISBN-10: 1098125975 ISBN-13: 978-1098125974

課程諮詢管道 For further questions, please get in touch with the teaching assistants.

延伸教學與資源

課程對應SDGs指標 SDG1：消除貧窮（No Poverty）
SDG4：優質教育（Quality Education）

課程是否導入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）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
● 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）

備註　

教師姓名	李文毅
Email	wy.lee@ntub.edu.tw
最後更新時間	2026-01-20 13:42:25
課程大綱	本課程整合微軟Azure Open AI雲端服務資源，運用問題分析能力進行實作與相關應用，以企業出題學生解題PBL模式導入人工智慧跨域專題實作，目的在幫助學生瞭解如何運用人工智慧技術來解決現實世界中的問題。本課程透過導入人工智慧在企業管理和決策、金融、數據分析、智慧製造等領域等應用案例，並以通用人工智慧跨域應用學習模式來跨域應用至數據分析、智慧運算、智慧製造及物流、商情預測、經營管理、金融等領域。透過本課程的修習，學生將獲得實際的跨學科專題實作能力，掌握人工智慧技術的應用和發展趨勢，養成專業職能和研發實力，並培育具有創業與創新精神人才，了解產業在數位轉型中，所面臨的困難與挑戰。 This course integrates resources from Microsoft Azure's Open AI cloud services, utilizing problem analysis skills for practical implementation and related applications. It introduces an industry-proposed, student-solved Project-Based Learning (PBL) model into the interdisciplinary project implementation of artificial intelligence. The aim is to help students understand how to apply AI technology to solve real-world problems. Through the introduction of artificial intelligence applications in enterprise management and decision-making, finance, data analysis, and smart manufacturing, the course enables students to gain practical interdisciplinary project implementation skills. This course also helps students understand the difficulties and challenges faced by industries in digital transformation.
課程進度 (1-16週)	Week 1. Introduction/The Fundamentals of Machine Learning Week 2. End-to-End Machine Learning Project/Classification Week 3. Training Models/Support Vector Machines Week 4. Decision Trees/ Ensemble Learning and Random Forests Week 5. Dimensionality Reduction/ Unsupervised Learning Techniques Week 6. Introduction to Artificial Neural Networks Week 7. Training Deep Neural Networks Week 8. Deep Neural Networks with PyTorch Week 9. Midterm Week 10. Deep learning related issues Week 11. Deep learning related issues Week 12. Azure Open AI Week 13. Azure Open AI Week 14. Azure Open AI Week 15. Azure Open AI Week 16. Azure Open AI Week 17. Azure Open AI Week 18. Final
評量方式與標準	20% Homework 30% Midterm 40% Final 20% Class participation
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 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 3rd 出版者: O'Reilly Media; 第3版 (2022年 11月 8日) 語言: English Paperback: 861 頁 ISBN-10: 1098125975 ISBN-13: 978-1098125974
課程諮詢管道	For further questions, please get in touch with the teaching assistants.
延伸教學與資源
課程對應SDGs指標	SDG1：消除貧窮（No Poverty） SDG4：優質教育（Quality Education）
課程是否導入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） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 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）
備註