| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| 使用雲端平台處理大數據與AI能力 |
| 1.Professional Knowledge and Practical Application |
| 2.Independent Analysis |
| 3.Innovative Research |
|
|
| Lecturing |
| Practicum |
| Discussion |
|
| Internship |
| Oral Presentation |
| Attendance |
| Written Presentation |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
雲端計算平台定義與生態系簡介 |
| Week 2 |
大數據簡介、雲端計算與服務生態系 |
| Week 3 |
Hadoop簡介、Hadoop HDFS分散式檔案系統與Hadoop MapReducer簡介 |
| Week 4 |
Spark與Scala語言簡介 |
| Week 5 |
Hadoop Single Node Cluster設置與執行 Multi Node Cluster 安裝、 設置與執行 |
| Week 6 |
Spark 的cluster模式架構圖與各種安裝模式 |
| Week 7 |
Spark RDD 介紹與RDD 的特性 |
| Week 8 |
RDD Key-Value 基本「轉換」運算與Key-Value「動作」運算 |
| Week 9 |
期中安裝實作 |
| Week 10 |
AI與機器學習簡介 |
| Week 11 |
推薦演算法與ALS 推薦演算法介紹與使用模型進行推薦 |
| Week 12 |
二元分類演算法與決策樹二元分類 |
| Week 13 |
資料準備階段、訓練評估階與預測階段 |
| Week 14 |
邏輯迴歸二元分類與邏輯迴歸分析 |
| Week 15 |
支援向量機器SVM 二元分類與演算法基本概念 |
| Week 16 |
製作專題報告
|
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
實作與繳交報告70%、口頭報告30%出席10%
評分依班上成績分布行調整 |
| 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 |
|
| Office Hours |
| 星期三 13:00- 15:00 |