本課程主要教授人工智慧之基本架構與應用，內容涵蓋機器學習、深度學習於醫療場域之分類、預測與數據量化等應用。同時，課程亦將介紹大語言模型於醫療場域之應用，如護士交班內容設計、臨床資訊整理與醫療文本生成等。課程將透過實作訓練，引導學生了解人工智慧模型之建立流程，包括資料前處理、模型訓練、模型評估與結果解讀，使學生能累積人工智慧模型分析與生成式模型開發之實務經驗，並培養其應用於智慧醫療場域之能力。

This course mainly teaches the basic architecture and application of artificial intelligence. The content covers the classification, prediction and data quantification of machine learning and deep learning in the medical field. At the same time, the course will also introduce the application of large language models in the medical field, such as nurse shift content design, clinical information collection, and medical text generation. Through practical training, the course will guide students to understand the process of building artificial intelligence models, including data pre-processing, model training, model evaluation and result interpretation, so that students can accumulate practical experience in artificial intelligence model analysis and generative model development, and develop their ability to apply it in the smart medical field.

1. Python資料科學自學聖經：不只是建模！用實戰帶你預測趨勢、找出問題與發現價值 (基峯,ACL065700) (暫訂)
2. 自編教材

1. Python data science self-study bible: not just modeling! Use practical experience to help you predict trends, identify problems and discover value (Jifeng, ACL065700) (tentative)
2. Self-compiled teaching materials