應用人工智慧於教育專題製作
臺北科技大學 114-2 學期課程，課號 360359。
課程大綱
本課程主要透過人工智慧相關技術主題的探討與業界實例之分享，包括: 知識表達、機器學習、深度學習等，實際運用機器學習工具如: TensorFlow、Keras、PyTorch等，並整合校務研究資料於專題課程，參與校務分析研究；考量資安的個資議題，擬將校內部份去識別化後之資料，經過編碼變造處理後於課堂使用，以教育相關大數據進行專題實作與分析。透過分組討論以及專題實作競賽，讓學生實際應用人工智慧工具與方法於教育資料分析。 The goal of this course is to discuss the technical topics and share industrial examples related to artificial intelligence, including: knowledge representation, machine learning, and deep learning. The students are required to utilize machine learning tools such as: TensorFlow, Keras, and PyTorch, to participate in the institutional repository research and analysis of related data in term projects. In consideration of privacy issues of personal information, the data will go through de-identification process for the usage purpose of educational big data analytics in term projects. With group discussions and project competition, students will be able to apply artificial intelligence tools and methods in educational data analysis.
課程進度
1. 課程簡介 (Course Overview) 2. [分組討論]: 人工智慧概論 - 基本觀念與工具 (Group Discussion: Artificial Intelligence - Basics and Tools) 3-7. [分組討論]: 知識表達及案例探討、機器學習、監督式學習及案例探討 (Group Discussion: Knowledge Representation and Case Study for Educational Open Data, Machine Learning, Supervised Learning and Case Study) 8. 邀請演講: 機器學習應用 (暫定) (Invited Talk: Applications of Machine Learning (tentative)) 9-10. [分組討論]: 非監督式學習及案例探討 (Group Discussion: Unsupervised Learning and Case Study) 11. 企業參訪 (暫定) (Visits to Industry (tentative)) 12-14. [分組討論]: 深度學習及案例探討、視覺化呈現與分析結果解讀 (Group Discussion: Deep Learning and Case Study, Visualization and Result Interpretation) 15-18. 期末專題實作競賽: 分組報告 (Term Project Competition: Presentation)
評量方式
1. 課堂、演講、參訪、分組討論與報告: 40% 2. 成果展覽與專題展示: 60%
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：是 1. Reference book: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th ed., Pearson, 2020. 2. Datasets: 國內外教育相關開放資料，包括: Kaggle[12]、教育雲eduLRS學習行為資料庫[1]、國家教育研究資料庫[6]等.