| 課程與核心能力關聯配比(%) |
課程目標之教學方法與評量方法 |
| 課程目標 |
核心能力 |
配比(%) |
教學方法 |
評量方法 |
| We will introduce advanced data mining techniques in this course and also introduce the Hadoop platform (Spark and MapReduce Programming Model) |
| 1.具備資訊科學素養、資訊理論與數學分析之能力 |
| 4.具備分析、設計與整合資訊應用系統之能力 |
| 6.具備自我學習、溝通協調與團隊合作之能力 |
| 7.具備資料蒐集、獨立思考、解決問題及研究創新之能力 |
|
|
|
|
| 授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習) |
| 週次 |
授課內容 |
| 第1週 |
Topics to be covered in this course
- Information-based Learning
- Similarity-based Learning
- Error-based Learning
- Probability-based Learning
- Neural-Network-based Learning (Deep Learning)
- Performance Evaluation
- Text Mining Techniques
- Hadoop Programming Model
- Spark Programming Model
Week1 Introduction
Week2 Hadoop Tutorial (1/3)
Week3 Hadoop Tutorial (2/3)
Week4 Hadoop Tutorial (3/3)
Week5 Spark Tutorial (1/2)
Week6 Spark Tutorial (2/2)
Week7 Spark Hadoop Installation
Week8 Spark Hadoop Installation
Week9 Midterm Exam
Week10 Information-based Learning: Decision Tree
Week11 Similarity-based Learning: Knn Classifier Algorithm
Week12 Probability-based Learning: Bayes Classifier (1/2)
Week13 Probability-based Learning: Bayes Classifier (2/2)
Week14 Error-based Learning: Logistic Regression
Week15 PM25 Final Project/Paper Presentation
Week16 QA Final Project/Paper Presentation
Week17 Paper Presentation
Week18 Final Exam
|
| 第2週 |
|
| 第3週 |
|
| 第4週 |
|
| 第5週 |
|
| 第6週 |
|
| 第7週 |
|
| 第8週 |
|
| 第9週 |
|
| 第10週 |
|
| 第11週 |
|
| 第12週 |
|
| 第13週 |
|
| 第14週 |
|
| 第15週 |
|
| 第16週 |
|
自主學習
內容 |
|
|
| 學習評量方式 |
Midterm Exam 20 %
- First Project 20 %
- Final Exam 20 %
- Second Project 20 %
- Assignments 20 %
|
| 教科書＆參考書目(書名、作者、書局、代理商、說明) |
Reference Book: https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics
課程教材（教師個人網址請列在本校內之網址）
(teaching aids & teacher's website)
ilearning, http://web.nchu.edu.tw/~yfan/
|
| 課程教材（教師個人網址請列在本校內之網址） |
| appointment by email |
| 課程輔導時間 |
| appointment by email |
| 聯合國全球永續發展目標(連結網址) |
|
|