Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2025 346402 Cross-Disciplinary Project Implementation of Artificial Intelligence 1 3.0 3 ★ Wen-Yi Lee
Chung, Chien-Ping 管理學院(大) 26 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	346402	Cross-Disciplinary Project Implementation of Artificial Intelligence	1	3.0	3	★	Wen-Yi Lee Chung, Chien-Ping	管理學院(大)	26	0

Course Plan：

Instructor Wen-Yi Lee

Email wy.lee@ntub.edu.tw

Last Updated 2025-02-04 12:44:13

Course Objective 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.

Course Schedule 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

Evaluation and grading policy 20% Homework 30% Midterm 40% Final 20% Class participation

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
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

The access to curricular consultation For further questions, please get in touch with the teaching assistants.

Expanding teaching and resources

The course corresponds to the SDGs SDG1：消除貧窮（No Poverty）
SDG4：優質教育（Quality Education）

Does the course incorporate AI

Note
　

Instructor	Wen-Yi Lee
Email	wy.lee@ntub.edu.tw
Last Updated	2025-02-04 12:44:13
Course Objective	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.
Course Schedule	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
Evaluation and grading policy	20% Homework 30% Midterm 40% Final 20% Class participation
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 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
The access to curricular consultation	For further questions, please get in touch with the teaching assistants.
Expanding teaching and resources
The course corresponds to the SDGs	SDG1：消除貧窮（No Poverty） SDG4：優質教育（Quality Education）
Does the course incorporate AI
Note