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.

Use LLM to learn *** and its impact

Research and development

• Marina Favaro and Jack Clark, When AI builds itself, Anthropic  
• 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. 

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
• 辦公室時間
• 宇翔 (jemmy) ：莊敬 302，週四 4 - 6 pm (email:  jemmy5588  at Gmail)
• Kiran (可嵐)：Tuesday 7 - 9 pm (email:  kiransughra14 at Gmail) (Please use English.) 
• 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。

On Call (隨時待命)

Anthony S. Fauci, On Call: A Doctor’s Journey in Public Service, Viking, 2024/06/18 (Available at CYCU library.)

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

第一次注意到沈候選人的發言是在講 1999，透過市民反映的內容和各區市議員的深度訪談，找出大家最關心的問題，這就是資料科學和系統化解決問題的方法。以下這個影片也是前所未見，透過學者專家的提問，提出對未來台北市政府治理的方向和方法；我也希望我的選區有如此優質的候選人。誠如學生說的，很多人選前一套、選後一套；也的確有不少走鐘的教授，讓我們很失望。希望當選市長後，能兌現他的承諾，例如利用選舉補助款成立台北研究院，規劃都市的未來、建立可長可久的制度。 

沈伯洋「市長，你給我聽好了」學者篇 (AI 字幕的錯誤不少，影片從 12 分開始)