課程大綱

課程資訊
學年期 Semester	1142	課程代碼 Course Code	TM5610701
課程名稱 Course Title	人工智慧與商業分析 Artificial Intelligence and Business Analytics
學分數 Credits	3	選必修 Required/Elective	選修 /半學年
上課時間教室 Time/Location	W8(TR-812) W9(TR-812) W10(TR-812)
先修課程 Prerequisite(s)
授課教師 Lecturer	黃啟祐 Chi-Yo Huang
專業核心能力 Core Professional Competencies	■解決問題之能力(Problem Solving) ■管理專業知識(Comprehensive management knowledge)

以下課程內容資料由授課教師提供
課程相關網址 Course Website	http://
課程宗旨 Objective of The Course	The course "Artificial Intelligence and Business Analytics" for Management of Technology students aims to connect advanced artificial intelligence (AI) technologies with real-world business uses. This syllabus at the graduate level covers a thorough curriculum that familiarizes students with the basics of AI, such as machine learning, natural language processing, robotics, and decision-making systems. Students will acquire the ability to utilize AI to address intricate business challenges, improve decision-making, and develop cutting-edge solutions across different industries by combining theoretical understanding with practical projects. The course will address ethical considerations, societal impacts, and future trends in AI to equip students to responsibly lead the integration of AI into business strategies. Students will examine practical uses of AI in marketing, finance, operations, and customer service through case studies, gaining a comprehensive understanding of AI's potential to revolutionize business operations. By the course's conclusion, students will have gained a comprehensive comprehension of AI technologies and their practical implications in business, preparing them with the necessary skills to excel as leaders in technology management.
課程大綱 Outline of Lectures	1. Introduction 2. Introduction to Python (1) 3. Introduction to Python (2) 4. Data Visualization 5. Dimension Reduction 6. Multiple Linear Regression 7. Logistic Regression 8. Evaluating Predictive Performance 9. Deep Learning 10. Association Rule Mining 11. Cluster Analysis 12. Social Network Analysis 13. Text Mining 14. Web Crawler (Temp speaker: Dr. Y.S. Kao) 15. Large Language Model (Temp speaker: Dr. C.C. Chuan) 16. Final Project Presentation Note: The course schedule may be adjusted based on students’ learning progress; guest speakers will be invited.
教科書 Textbook	Shmueli, G., Bruce, P. C., Deokar, K. R., & Patel, N. R. (2025). Machine learning for business analytics: Concepts, techniques, and applications in Python (2nd ed.). John Wiley & Sons.
參考書目 References	Allen, G. (2020). Understanding AI technology. Joint Artificial Intelligence Center. Dawson, M. (2010). Python programming for the absolute beginner (3rd ed.). Course Technology. Rose, D. (2020). Artificial intelligence for business. FT Press.
修課學生須知 Notice of course enrollment	N/A
評量方式 Grading	Grading Policy: Participation and class attendance: 30%. Home Works: 30% Term Paper: 40%. Unless it is impossible to ask for leave in advance due to force majeure, an absence will result in a 10-point deduction from the total score.
備註說明 Note