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 |
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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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核心能力關聯 |
核心能力與課程規劃關聯圖 |
課程大綱
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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
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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
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預期每週課後學習時數 |
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Office Hours |
備註: TBD |
指定閱讀 |
Lecture notes
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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% |
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2. |
Final Exam |
30% |
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3. |
Term Project |
30% |
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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) |
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