人工智慧跨領域專題實作
臺北科技大學 114-2 學期課程，課號 360352。
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
Based on a comprehensive score of class attendance and course participation
教材
【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.