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.

一些常聽的 Podcast 節目和培養英文聽力的方法

一些常聽的 Podcast 節目 (*)，適合坐車、(讓眼睛) 休息、睡前、或運動時聽 (Some frequently listened-to podcasts (*) suitable for listening while traveling, giving your eyes a rest, before bed, or during exercise.) 

• Acquired (You could look at its transcript using iPhone.)

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. 

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)

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

Foundations of Computer Vision

Antonio Torralba, Phillip Isola, and William Freeman, Foundations of Computer Vision, The MIT Press, 2024.

作者是三位 MIT 的教授。如果你想了解電腦視覺 (Computer vision) 相關研究，甚至機器學習、研究方法，大力推荐。我有空就翻一下，增加對整個領域的了解，每次都有驚喜，很神奇的書。例如

Yearly summarization of Google AI research coauthored by Jeff Dean and more

Jeff Dean (google scholar)

AI achieves silver-medal standard solving International Mathematical Olympiad problems

AlphaProof and AlphaGeometry teams, AI achieves silver-medal standard solving International Mathematical Olympiad problems, 25 JULY 2024.

Chip Placement with Diffusion

Vint Lee, Chun Deng, Leena Elzeiny, Pieter Abbeel, and John Wawrzynek, Chip Placement with Diffusion, arXiv:2407.12282.