課程資訊
課程名稱
 製造數據科學
Manufacturing Data Science 
開課學期
 109-1 
授課對象
 管理學院  資訊管理學系  
授課教師
 李家岩 
課號
 IM5055 
課程識別碼
 725 U3660 
班次
  
學分
 3.0 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期二7,8,9(14:20~17:20) 
上課地點
 管二301 
備註
 商業資料分析學分學程課程。
限學士班三年級以上
總人數上限：70人 
課程簡介影片
 
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課程概述

MDS course supports students learning how to apply artificial intelligence (AI), machine learning, data science (DS) techniques to improve the effectiveness and efficiency of the manufacturing systems. MDS integrates the knowledge domains of the information, engineering, and management. Encourage students to solve the real problem systematically using the design of analytics, from descriptive, diagnostic, predictive, prescriptive to automating, for successfully enhancing decision quality. 

課程目標
 1. Learn the statistical learning and optimization methodologies for intelligent manufacturing systems
2. Create a prototype model to solve the problem in real setting related to manufacturing or service systems
3. Develop the research skills and prepare a analytical project report
 
課程要求
 1. It's better to have prerequisite courses: (1) probability and statistics; (2) operations research
2. Python programming skills
3. Students need to read literature and develop analytical model for term project

 
預期每週課後學習時數
  
Office Hours
 備註： TBD 
指定閱讀
 Lecture notes
 
參考書目
 Hastie, T., R. Tibshirani, and J. Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer.
Hillier, F. S., Lieberman, G. J. (2010), Introduction to Operations Research, 9th ed., McGraw-Hill, New York.
Hopp, W. and M. Spearman (2011), Factory Physics, 3rd ed., Waveland Press.
Montgomery, D. C. (2013), Introduction to Statistical Quality Control, 7 ed.: John Wiley & Sons, Inc.
Nahmias, S. (2008), Production and Operations Analysis, 6th ed., McGraw-Hill/Irwin.
Pinedo, M. L. (2016), Scheduling: Theory, Algorithms, and Systems, 5th edition, Springer-Verlag New York. 
評量方式
(僅供參考)
  
No.
項目
百分比
說明
1. 
 Homework Assignment 
40% 
  
2. 
 Final Exam 
30% 
  
3. 
 Term Project 
30% 
  
 
課程進度
週次
日期
單元主題
第1週
 9/15  Data Science & Manufacturing Systems 
第2週
 9/22  Descriptive Analytics- Data, Information, Knowledge, and Data Preprocessing 
第3週
 9/29  Descriptive Analytics- Feature Engineering and Data Augmentation 
第4週
 10/06  Descriptive Analytics- Feature Selection and Dimension Reduction 
第5週
 10/13  Manufacturing Practice I 
第6週
 10/20  Diagnostic and Predictive Analytics- Regression, Classification, MARS, and Symbolic Regression 
第7週
 10/27  Diagnostic and Predictive Analytics- Tree-based Methods, Random Forest and Boosting 
第8週
 11/03  Diagnostic and Predictive Analytics- Statistical Process Control and Signal Processing 
第9週
 11/10  Literature Review Project 
第10週
 11/17  Diagnostic and Predictive Analytics: Clustering Analysis and Deep Learning 
第11週
 11/24  Manufacturing Practice II 
第12週
 12/01  Prescriptive Analytics- Linear Programming and Dynamic Programming 
第13週
 12/08  Prescriptive Analytics- Metaheuristic Algorithm and Genetic Algorithm 
第14週
 12/15  Prescriptive Analytics- Scheduling Optimization and Run-to-Run Control 
第15週
 12/22  Prescriptive Analytics- Markov Decision Process and Reinforcement Learning 
第16週
 12/29  Final Exam 
第17週
 1/05  期末報告 (Term-project Presentation)