人工智慧技術與商業應用

課程大綱

(Part I) 1. AI 基礎概念與類神經網路初體驗 2. AI 學習原理與模型錯誤直覺解析 3. 圖像辨識核心概念與應用場景 4. 圖像辨識實作：CNN 遷移學習應用 5. 自然語言處理（NLP）與文本應用概論 6. 生成對抗網路（GAN）原理與應用 7. 專家分享：企業 AI 導入挑戰與案例 8. AI 決策建模與深度強化學習應用 (Part II) 內容以哈佛個案授課方法為主，修課同學每次上課前均需閱讀一個產業個案，同時研究個案討論問題，本課程上課時將分成三個階段，第一階段為分組小組討論，對個案討論問題進行分析，第二階段為全課堂討論時間，授課教師將引導修課同學進行個案分析，最後一階段為課末小組報告，同時作為課程總結。 (Part I) 1. AI Fundamentals and Neural Network Hands-on 2. AI Learning Principles and Model Intuition 3. Core Concepts and Applications of Image Recognition 4. Image Recognition Practice: CNN Transfer Learning 5. Introduction to NLP and Text Applications 6. GAN Fundamentals and Practical Applications 7. Guest Talk: Challenges and Cases in AI Adoption 8. AI Decision Modeling and Deep Reinforcement Learning (Part II) The course is primarily conducted using the Harvard case teaching method. Before each class, students are required to read an industry case and study the accompanying discussion questions. The class is divided into three stages: in the first stage, students engage in small group discussions to analyze the case questions; in the second stage, the entire class participates in a discussion led by the instructor to further analyze the case; and in the final stage, each group presents a report at the end of the class, serving as the course conclusion.