| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| 使用雲端平台處理IOT與AI資料能力 |
|
|
| Exercises |
| Discussion |
| Lecturing |
|
| Written Presentation |
| Attendance |
| Oral Presentation |
| Internship |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
大數據、物聯網、人工智慧與機器學習
|
| Week 2 |
雲端計算與服務生態系 |
| Week 3 |
Hadoop Single Node Cluster設置與執行(I) |
| Week 4 |
Hadoop Single Node Cluster設置與執行(II) |
| Week 5 |
Hadoop Single Node Cluster設置與執行(III) |
| Week 6 |
Multi Node Cluster 安裝、 設置與執行(I) |
| Week 7 |
Multi Node Cluster 安裝、 設置與執行(II) |
| Week 8 |
Spark 的cluster模式架構圖與各種安裝模式 |
| Week 9 |
Spark RDD 介紹與RDD 的特性 |
| Week 10 |
RDD Key-Value 基本「轉換」運算與Key-Value「動作」運算 |
| Week 11 |
RDD Key-Value 基本「轉換」運算與Key-Value「動作」運算實作 |
| Week 12 |
期中安裝實作
|
| Week 13 |
AI與機器學習簡介、 推薦演算法與ALS 推薦演算法介紹與使用模型進行推薦 |
| Week 14 |
二元分類演算法與決策樹二元分類
|
| Week 15 |
資料準備階段、訓練評估階與預測階段
|
| Week 16 |
邏輯迴歸二元分類與邏輯迴歸分析
支援向量機器SVM 二元分類與演算法基本概念
單純貝氏二元分類與單純貝氏分析原理簡介
決策樹多元分類與「森林覆蓋樹種」大數據問題分析情境
期末報告
期末報告 |
self-directed
learning |
|
|
| Evaluation |
| 實作與繳交報告 小組報告 |
| Textbook & other References |
| 書名：Python+Spark+Hadoop 機器學習與大數據分析實戰 林大貴 博碩書局 |
| Teaching Aids & Teacher's Website |
| ilearning3 |
| Office Hours |
| 週四 16:00-18:00 |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|