Dynamic Programming and Reinforcement Learning (動態規劃和強化學習)

• Course objective: This course introduces dynamic decision-making under uncertainty, with an emphasis on dynamic programming and reinforcement learning. Drawing on applications in business and engineering, students will learn key theories and algorithms for solving multi-stage decision problems, both with and without explicit models of the environment. Assignments and a final project provide practical experience in problem formulation, algorithm evaluation, and Python-based implementation.

(Plz read it first) Business Analytics Laboratory (商業分析實驗室)

(Important) Please read this webpage first if you want to join our lab and schdule an interview.

(26/9/8) We welcome 育輔 (Dave) to the lab!

(26/9/5) 恭喜振宇 (Jeff)，榮獲 2026 第十九屆崇越論文大賞佳作獎 (the 19th TSC Thesis Award) (photos)

(26/8/26) We welcome Kiran, 晏華 (YenHua), and Chynna to the lab!

(26/8/6) New research results (COSMOS)

Nice talks on AI, geopolitics, optimization, SCM, and more

Jeff Dean, 2026 Frontier & Pioneer Symposium, Asian American Scholar Forum, 2026/8/18

• 20:50 -- 26:20: It is better to skim 10 papers than to read one in detail because you kind of then get 10 points in your cloud of like what might be possible. Or even skim a 100 abstracts because what you want to be able to do is connect you know important idead that have not yet been connected.... Try lots of things that might not work. Some of them will.
• Taking fundamental courses in a field is important so you have a solid background to engage in research or development. For beginners in research, thoroughly reading a high-quality paper is essential to master the research problem and understand the technical presentation.
• You can track researchers in your research field using Google Scholar. Best paper awards in top journals or conferences are also excellent sources. You can also search for "classic or famous paper[s] in [your field]" to find the fundamental papers in that field.
• The engineers need to read one paper per week, and five persons need to present their experiments in one AI department with 100 persons at TSMC.
• Reading history books to understand human nature and society can be highly useful.
• 45:42 -- end: Good question and answer
• Jeff Dean and Sanjay Ghemawat, Performance Hints, Original version: 2023/07/27, last updated: 2025/12/16

Subject to: Dimitris Bertsimas

Subject to: Dimitris Bertsimas

References for graduate students and my laboratory members

Knowledge to master for a better foundation (and future)

Tools and general: 

•  Adi Ignatius, “We Want to Make Ourselves Better”: The HBR Interview with Bob Sternfels, Harvard Business Review, January–February 2026. 

上課內容 (Teaching)

許志華，中原大學，電機資訊學院，工業與系統工程學系

• Please use the school's MS team account for online sessions. 
• Login Account: 學號@o365st.cycu.edu.tw, student_ID@o365st.cycu.edu.tw
• Please use browser-based login instead of the application program to avoid login problems.
• Please be considerate/polite, and show up only during the office hours of a course. The students in the lab need quiet time for their studies and research. 
• 講義上有的內容，請同學要先查看或聽影片。有問題或疑義，再詢問助教。
• Email: If you have further questions, please email the TA. (cc (副本) teacher: chhsu135 at Gmail) TA will handle your questions or concerns. TA will discuss with me if she/he cannot resolve the issues.
• 2026 秋 (Fall) 
• Dynamic Programming and Reinforcement Learning (動態規劃和強化學習)，博士班 (PhD)
• Form for you to fill in
• 助教：廖庭煜 (Angus)
• 作業研究 (上)，工業三乙
• 登記，課前預習和每週進度
• 助教：  (email:   at Gmail)，  ( ) (email:    at Gmail) 
• 辦公室時間：莊敬 302， (一) 15-17 pm， (二) 13-15 pm
• 翻轉教室 (Flipped classroom)，中原隨筆 (My days at CYCU)
• 學習數學的四個層次，專題和論文的製作與報告 (tips for the final project and your thesis)，上台報告的方法與建議。
• 課程組員互評表 (Evaluation form for team members)
• Deadline: the day for the last class meeting time
• Default: If no one answers, everyone will get the same score for project participation.  If only 1 person fills in the form, we will use that as the group score for project participation.
• The best way to contact me (email):  chhsu135 AT gmail DOT com (主旨：修課名稱和班級，學號，姓名) (Subject: Class, student ID, Name)
• 辦公室 (My office)：(工業系館) 莊敬大樓 (Building no. 23 for ISE) 201-3，電話 (phone)  03 265 4427。辦公室時間，四 3 - 5:50 pm (歡迎使用 Microsoft team)。導生 (分組登記)。FB，Threads。

研究 (Research)

Journal articles:

• P.-S. Chen, H.-Y. Liu, and C.-H. Hsu, Economic Order Quantity of Deteriorating Drugs with New and Redistributed Demands in a Closed-loop Supply Chain: From the Viewpoint of a Hospital Pharmacy, International Journal of Systems Science: Operations & Logistics, 2026, Vol. 13, No. 1. (SCIE) 
• P.-S. Chen, J. Lyu,  C.-W. Chen, I.-H. Yu, and C.-H. Hsu, The Social-economic Value of a Renewable Energy Power Generation and Energy Storage System: A Case Study, International Journal of Systems Science: Operations & Logistics, 2026, Vol. 13, No. 1. (SCIE)
• H.-A. Bui, C.-H. Hsu, H.-W.V. Young, Y.-Y. Chen, and Y.-A. Liou, Advanced Semi-Supervised Learning for Remote Sensing-Based Land Cover Classification in the Mekong River Delta, Vietnam, Remote Sensing, 2026, 18, 989. https://doi.org/10.3390/rs18070989. (SCIE)

服務和計畫 (Service and Projects)

政府計畫 (Government Project)

• 國科會，高維度受限迴歸模型與稀疏特徵選取：快速座標下降演算法和應用 (High-Dimensional Constrained Regression Models and Sparse Feature Selection: Fast Coordinate Descent Algorithms and Applications)，主持人 (PI)，115 年度 (2026/8 - 2027/7)
• 國科會，公平最佳決策樹和其應用 (Fair and Optimal Decision Tree and Its Applications)，主持人 (PI)，114 年度 (2025/8 - 2026/7)

產學合作 (Industry-Academia Cooperation)

• 金屬工業研究發展中心 (Metal Industries Research & Development Centre)，最佳化決策於工站排程應用之研究 (Optimal Decision-making in Work Station Scheduling) (3)，主持人 (PI)，2024

中原隨筆 (My days at CYCU)

在台南住了20幾年，因為種種因素，來到中原大學任教。懷念台南的好天氣、上下班不太塞車、和美食。秉持共好的精神，紀錄我在中原的所見所聞。

• 商業分析實驗室 (Business Analytics Laboratory)
• 指引 (Guides)，論文報告 (Group meeting)
• 新課：Dynamic Programming and Reinforcement Learning (動態規劃和強化學習)，Nonlinear Optimization (非線性最佳化)

作業研究期末報告

優秀作品 (內容和投影片製作)

• 戴維萱、彭彥智、蔣咏錡、鍾定豊、黃郁潔，冷凍食品配送車輛路徑問題的最佳化模型(2026 春作業研究 (下)，學科競賽版本)
• 林昱綺、李昀諺、嚴振瑋、許耀倫、蕭瑋萱，結合蟻群演算法之物流配送路徑最佳化研究 (2026 春作業研究 (下)，學科競賽版本)
• 胡嘉豪、陳昱安、彭琬婷、呂仲皓，醫院護理人員排班最佳化之應用 (2026 春作業研究 (下)，學科競賽版本)

Use LLM to learn *** and its impact

Research and development

• Marina Favaro and Jack Clark, When AI builds itself, Anthropic (new)
• As of May 2026, more than 80% of the code we merge into Anthropic’s codebase was authored by Claude.
• In the second quarter of 2026, the typical engineer was merging 8× as much code per day as they were in 2024.
• On the most open-ended tasks, Claude’s success rate reached 76% in May 2026, up 50 percentage points in six months.
• In this world, the pace of progress in AI development becomes determined entirely by the availability of compute (or the speed of discovering various efficiencies in algorithmic training or inference) for AI systems. Humans play a substantially diminished role in their development, likely moving most of our effort towards oversight, validation, and verification of an expanding “virtual lab” run by AI systems. We expect that systems capable of automated AI research and development would have skills that would transfer to the rest of science, allowing them to begin to revolutionize other fields.
• Dimitris Bertsimas and Georgios Margaritis, Robust and Adaptive Optimization under a Large Language Model Lens, arXiv:2501.00568. 

Nonlinear Optimization (非線性最佳化)

• Course objective: Nonlinear optimization is widely used in engineering, business, data science, and machine learning. We will introduce fundamental algorithms through examples in this course, enabling students to read research articles, formulate their own problems, and solve them efficiently using the appropriate algorithms.

資料科學相關研究所和職涯準備

基礎知識：工業系必修課程 

• 資料科學：領域知識 (智慧製造，量化投資，行銷等等)，數學，資訊科技 
• 如果不會寫 for loop 和 function：自學 Python (每一講有作業)
• 很棒的免費課程：CS50's Introduction to Programming with Python (Malan at Harvard), Mathematics for Machine Learning and Data Science (at Coursera)
• 發展和待遇：機器學習，AI 工程師，最佳化演算法，optimization，量化交易 (方法)，智慧製造演算法，作業研究 (台達電，薪水，奇點無限 )，資料科學家
• 謝謝庭煜

上：

• 核心：IE623G 機器學習，IC258D 資料結構 (查怡老師)，CS361L 資料庫系統 
• 庭煜跟我念 4 + 1，修資料結構，拿到全班第一名，系上還提供一萬元的獎金。大四也有修資料庫系統，和此文章中許多難的課程，加上努力，所以研究做得好。據我所知，只有他聽進去我的建議。
• 數學：MA305G 離散數學，MA202 高等微積分(一)，MA306G 數值分析
• 資訊科技：IE440G 系統模擬 (包含數學和領域知識)，IE250D 網路原理與應用，IE302G 製造聯網整合技術
• 領域知識
• 作業管理 (含智慧製造)：IE238G 電腦輔助設計與製造等等
• 量化交易：FA319 財務管理(一)，FA295E 金融市場，FA082G 投資學等等
• 行銷：BA245 行銷管理學，BA336 網路行銷，IU209G 消費者行為 (和 IT911D 二選一) 等等

下：

• 核心：IE326G 人工智慧導論，IE371G 實驗設計，EL602L 計算機演算法
• 數學：MA202 高等微積分(二)，MA203 微分方程(一)
• 資訊科技：MA405M 圖論演算法，IE582G 視覺系統
• 領域知識
• 作業管理 (含智慧製造)：IE431G 電腦整合製造，IE527G 物聯網與大數據於智慧製造應用 等等
• 量化交易：FA320 財務管理 (二)，FA011 金融科技，FA323 區塊鏈與加密貨幣，FA447 期貨與選擇權等等
• 行銷：IT911D 消費者行為等等

中原大學 x 台積電半導體智慧製造學程。

預備研究生 (4 + 1) 

• 獎學金和支援
• 節省一年時間。
• 學校：
• 歷年平均成績系排名在前 30% 者，發給獎學金貳萬元整。獎學金於學生就讀碩士班一年級依學期分 2 次發给。
• 全人榮譽獎，全人標竿獎。
• 系上：
• 歷年平均成績系排名在前 20% 者，發給獎學金十萬元整。
• 參與國外國際研討會與競賽之報名費、交通費、住宿費與生活費，至多兩萬元。
• 商業分析實驗室 (Business Analytics Laboratory)：論文。
• 人少，老師 (每週) 單獨討論。學生的光，老師看得見。
• 根據經驗，班排不是一個絕對的指標。重點在於想要 (投入時間) 改變自己。
• 可詢問庭煜，準時畢業且有良好的研究成果。建議從三下開始；庭煜從三下準備校外機器學習競賽、暑假實習和我做研究。
• Guides for students in Business Analytics Laboratory (實驗室學生指引)：修課。
• Nice quality to have: Enjoy learning and coding (or willing to), think independently, willing to share and help
• 表現優良，老師將提供研究助理費。

資訊

• 同學的問題：我們 (工業系) 從事軟體開發和資工系的差別？
• 我：如果想要從事軟體開發的 (高薪) 工作，量化學科的基礎 (例如機統、作業研究)、流程和系統的思維 (例如生產計劃與管制、品質管制、人因工程)、和商管的知識背景 (例如經濟學、會計)，是工業系的優勢。只要加上軟體系統開發需要的課程，例如資料結構、資料庫、(資管) 系統分析與設計 (開放式課程)、演算法、物件導向分析與設計、作業系統、離散數學等等，就可如虎添翼。工業系畢業學長姐，目前 (2023) 擔任純網銀的董事長和台北市資訊局副局長，就是最好的例證。
• 舉例說明工業系的優勢：線上購物定價的需求函數和最佳化，資訊系統的人機介面 (可以利用機統、實驗設計、機器學習做進階分析)，熱門售票活動中的 (軟體系統) 瓶頸等等。
• 曾子軒，旺宏吳敏求：AI 時代與其學程式，懂應用數學才是業界最想要人才，遠見，2023-11-10
• 一方面感念母校栽培，一方面從個人經驗中認知到統計學，甚至是人工智慧的重要，吳敏求因此效法黑石集團 (Blackstone Group) 創辦人蘇世民 (Stephen Schwarzman) 捐贈麻省理工學院蘇世民運算學院 (MIT Schwarzman College of Computing，簡稱 MIT 運算學院) 的作法，捐贈成大設置敏求智慧運算學院。
• 數學邏輯和程式語言的邏輯是相通的。
• Brian Christian and Tom Griffiths, Algorithms to Live By: The Computer Science of Human Decisions, William Collins, 2017. 甘錫安譯，決斷的演算：預測、分析與好決定的 11 堂邏輯課，行路，2017

2025 Franz Edelman Award

2025 Edelman Competition (video, special issue in the INFORMS Journal on Applied Analytics)

Ryan Cooper, Lindsay Golich, Craig Griffin, Jono Hailstone, Jim Miller, Gary Sutton (2026) Project 4:05: Optimizing USA Cycling’s Women’s Team Pursuit Gold. INFORMS Journal on Applied Analytics 56(1):5-22. (2025 Franz Edelman Award Winner) (Keywords: Edelman Award team pursuit, operations research aerodynamics, optimization, mixed-integer programming, sports analytics, Olympic cycling)

Some books and information on machine learning and AI

General introduction (without math)

• 簡禎富，工業3.5：台灣企業邁向智慧製造與數位決策的戰略，天下雜誌，2019
• Alex J. Gutman and Jordan Goldmeier, Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, Wiley, 2021.

Tabular foundation model

Hollmann, N., Müller, S., Purucker, L. et al. Accurate predictions on small data with a tabular foundation model. Nature 637, 319–326 (2025). https://doi.org/10.1038/s41586-024-08328-6 (Code)

The most-cited papers of the twenty-first century

Helen Pearson, Heidi Ledford, Matthew Hutson & Richard Van Noorden, Exclusive: the most-cited papers of the twenty-first century: A Nature analysis reveals the 25 highest-cited papers published this century and explores why they are breaking records, Nature, 15 April 2025. (Supplementary information)

Papers in AI:

Adaptive Forests For Classification

D. Bertsimas and Y. Cui, Adaptive Forests For Classification, arXiv:2510.22991, 2025. (code)

Data-Driven Science and Engineering

Steven L. Brunton and J. Nathan Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, 2nd edition, Cambridge University Press (pdf, amazing videos, connection with dynamical systems in engineering)

作業研究和機器學習

課程

• 作業研究 (上): 3.8 迴歸，11.2 迴歸，12.3.1 類神經
• Google Scholar: generative ai optimization
• 作業研究 (下): 2 Robust Optimization (穩健最佳化)，3 適應穩健最佳化，5 資料驅動的報童模型
• Applications in generative AI: diffusion probabilistic models 
• Dmytro Kuzmenko, Denoising diffusion probabilistic models
• Lilian Weng, What are Diffusion Models?
• Dimitris Bertsimas and Georgios Margaritis, Robust and Adaptive Optimization under a Large Language Model Lens, arXiv:2501.00568.
• 最佳化和機器學習：

  機器學習和作業研究的奇妙結合