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課程名稱 |
製造數據科學
Manufacturing Data Science |
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開課學期 |
112-1 |
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授課對象 |
學程 商業資料分析學分學程 |
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授課教師 |
李家岩 |
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課號 |
IM5055 |
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課程識別碼 |
725 U3660 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期五2,3,4(9:10~12:10) |
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上課地點 |
管二101 |
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備註 |
先修課程：統計學。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 |
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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 |
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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.
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欲加簽同學請參加第一堂實體課程，謝謝。 |
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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 |
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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.
李家岩、洪佑鑫 (2022)，製造數據科學，前程文化。 |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
作業 |
50% |
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2. |
專題實作 |
50% |
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針對學生困難提供學生調整方式 |
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上課形式 |
以錄音輔助, 以錄影輔助 |
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作業繳交方式 |
團體報告取代個人報告 |
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考試形式 |
書面(口頭)報告取代考試 |
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其他 |
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週次 |
日期 |
單元主題 |
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第1週 |
9/08 |
Introduction (製造數據科學) |
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第2週 |
9/15 |
Association Rules (關聯規則) |
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第3週 |
9/22 |
Manufacturing System Management (製造系統分析與管理) |
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第4週 |
9/29 |
Foundation and Model Evaluation (數據科學基礎與模型評估) |
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第5週 |
10/06 |
Data Science Framework (數據科學分析架構) |
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第6週 |
10/13 |
Data Preprocessing (數據預處理) |
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第7週 |
10/20 |
Decision Tree and Ensemble Methods (決策樹與集成學習) |
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第8週 |
10/27 |
Decision Tree and Ensemble Methods (決策樹與集成學習) |
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第9週 |
11/03 |
Manufacturing Practice (產業專家演講) |
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第10週 |
11/10 |
Feature Selection and Dimension Reduction (特徵挑選與維度縮減) |
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第11週 |
11/17 |
Feature Engineering and Data Imbalance (特徵工程、數據增強與數據平衡) |
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第12週 |
11/24 |
Prognostics and Health Management (故障預測與健康管理) |
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第13週 |
12/01 |
Metaheuristics (元啟發式演算法) |
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第14週 |
12/08 |
Reinforcement Learning(強化學習) |
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第15週 |
12/15 |
Business and Industry Applications |
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第16週 |
12/22 |
Final Project |
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第17週 |
12/29 |
No Class |
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第18週 |
1/05 |
No Class |
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