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 360352 人工智慧跨領域專題實作 1 3.0 3 ★ 范育成
彭朋群
蔣欣翰 AI雙聯專班 15 0 3/22.28; 4/18; 5/9.16.30

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	360352	人工智慧跨領域專題實作	1	3.0	3	★	范育成彭朋群蔣欣翰	AI雙聯專班	15	0	3/22.28; 4/18; 5/9.16.30

教學大綱與進度：

教師姓名范育成　教師諮商時間(Office Hours)

Email skystar@ntut.edu.tw

最後更新時間 2026-02-12 21:52:05

課程大綱 This course is based on the management problems that students encounter at work, including operation research (OR), production planning and management, financial analysis, etc., and explores the technical principles of using machine learning, as well as the feasibility and scalability of application in these fields. The discussion scope of the machine learning will not only cover the most basic concepts and principles of convolutional neural networks (CNN), self-attention, transformer, generative model, large language models (LLM), linear programming, and probabilistic programming, but also guide students to understand the importance of one or more of these architectures. In terms of teaching aids, in response to the trend of the times, the programming development environment will be combined with the LLM approach to make it easier for students to quickly get started with programming languages. While looking at their own examples to achieve their own results, they will have a higher sense of accomplishment. The students also can accept the meaning of LLM’s most popular prompt engineering and parameter quantities. In addition, we will discuss some AI case studies, including applications of AI in medical, verbal communication, autonomous driving, education, and in multiple fields. Students will be able to apply AI technology to various fields. This course uses a problem-based learning (PBL) approach to tackle real- world business issues, guiding students through interdisciplinary AI projects. It aims to teach students how to apply AI technologies such as machine learning, image recognition, and natural language processing across various domains like data analysis, healthcare, and smart manufacturing. Students will gain practical skills, enhance problem-solving abilities, and develop innovative thinking. The course prepares students to handle AI projects, understand industry challenges, and cultivate entrepreneurial and innovative spirits.

課程進度 A. History and Origin of OR B. Basic concepts of production planning and control C. Financial Management and Quantitative Science D. Differences and discussions between basic probability and statistics and Bayesian statistics E. Models of artificial intelligence Tools and Techniques: a. Programming language b. LLM prompt engineering and common prompts c. Google Colaboratory and Gemini d. How to analyze and preprocess the data form and content given to the computer In terms of teaching aids, in response to the trend of the times, the programming development environment will be combined with the LLM approach to make it easier for students to quickly get started with programming languages. While looking at their own examples to achieve their own results, they will have a higher sense of accomplishment. The students also can accept the meaning of LLM’s most popular prompt engineering and parameter quantities. In addition, we will discuss some AI case studies, including applications of AI in medical, verbal communication, autonomous driving, education, and in multiple fields. Students will be able to apply AI technology to various fields.

評量方式與標準 Based on a comprehensive score of class attendance and course 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：否
1.Frederick S. Hillier, Gerald J. Lieberman, “Introduction to Operations Research” 2.Stephen N. Chapman, “The fundamentals of production planning and control” 3.QuantInsti Quantitative Learning Pvt. Ltd., “Python Basics with Illustrations from the Financial Markets” 4.Other related documents and briefings are provided at separate download URLs.

課程諮詢管道 Office Hours: Mon. 345678

延伸教學與資源

課程對應SDGs指標 SDG3：良好健康和福祉（Good Health and Well-being）
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）
SDG12：負責任的消費與生產（Responsible Consumption and Production）
SDG16：和平正義與有力的制度（Peace, Justice and Strong Institutions）

課程是否導入AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

備註

教師姓名	范育成　教師諮商時間(Office Hours)
Email	skystar@ntut.edu.tw
最後更新時間	2026-02-12 21:52:05
課程大綱	This course is based on the management problems that students encounter at work, including operation research (OR), production planning and management, financial analysis, etc., and explores the technical principles of using machine learning, as well as the feasibility and scalability of application in these fields. The discussion scope of the machine learning will not only cover the most basic concepts and principles of convolutional neural networks (CNN), self-attention, transformer, generative model, large language models (LLM), linear programming, and probabilistic programming, but also guide students to understand the importance of one or more of these architectures. In terms of teaching aids, in response to the trend of the times, the programming development environment will be combined with the LLM approach to make it easier for students to quickly get started with programming languages. While looking at their own examples to achieve their own results, they will have a higher sense of accomplishment. The students also can accept the meaning of LLM’s most popular prompt engineering and parameter quantities. In addition, we will discuss some AI case studies, including applications of AI in medical, verbal communication, autonomous driving, education, and in multiple fields. Students will be able to apply AI technology to various fields. This course uses a problem-based learning (PBL) approach to tackle real- world business issues, guiding students through interdisciplinary AI projects. It aims to teach students how to apply AI technologies such as machine learning, image recognition, and natural language processing across various domains like data analysis, healthcare, and smart manufacturing. Students will gain practical skills, enhance problem-solving abilities, and develop innovative thinking. The course prepares students to handle AI projects, understand industry challenges, and cultivate entrepreneurial and innovative spirits.
課程進度	A. History and Origin of OR B. Basic concepts of production planning and control C. Financial Management and Quantitative Science D. Differences and discussions between basic probability and statistics and Bayesian statistics E. Models of artificial intelligence Tools and Techniques: a. Programming language b. LLM prompt engineering and common prompts c. Google Colaboratory and Gemini d. How to analyze and preprocess the data form and content given to the computer In terms of teaching aids, in response to the trend of the times, the programming development environment will be combined with the LLM approach to make it easier for students to quickly get started with programming languages. While looking at their own examples to achieve their own results, they will have a higher sense of accomplishment. The students also can accept the meaning of LLM’s most popular prompt engineering and parameter quantities. In addition, we will discuss some AI case studies, including applications of AI in medical, verbal communication, autonomous driving, education, and in multiple fields. Students will be able to apply AI technology to various fields.
評量方式與標準	Based on a comprehensive score of class attendance and course 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：否 1.Frederick S. Hillier, Gerald J. Lieberman, “Introduction to Operations Research” 2.Stephen N. Chapman, “The fundamentals of production planning and control” 3.QuantInsti Quantitative Learning Pvt. Ltd., “Python Basics with Illustrations from the Financial Markets” 4.Other related documents and briefings are provided at separate download URLs.
課程諮詢管道	Office Hours: Mon. 345678
延伸教學與資源
課程對應SDGs指標	SDG3：良好健康和福祉（Good Health and Well-being） 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） SDG12：負責任的消費與生產（Responsible Consumption and Production） SDG16：和平正義與有力的制度（Peace, Justice and Strong Institutions）
課程是否導入AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
備註