專題和論文的製作與報告 (tips for the final project and your thesis)

• (at the bottom) Avoid common phenomena, final written report (for your ppt content)
• (大學生) 重要任務

• 人生困境：

• 計算機程式補考後的人生 (1/2) 

• 我的方法：運動，音樂 (馬友友)，閱讀 (哲學，堅忍的挑戰，老莊)

• Tal Ben-Shahar, Happier: Learn the Secrets to Daily Joy and Lasting Fulfillment, McGraw Hill, 2007. (譚家瑜譯，更快樂：哈佛最受歡迎的一堂課，天下雜誌，2012)

工業系同學的起薪

長期關心青年低薪的問題。根據大規模的數據和個人的經驗，教育是脫離貧窮的有效方法 (1)。

這幾天收到學校的資料，看到這些統計數字，分享一下。

AI 養魚解決老師傅技術失傳問題

邱倢芯，從魚缸到金目鱸養殖場，AI 養魚解決老師傅技術失傳問題，科技新報，2023 年 11 月 09 日

對於建置一套系統，許多業主最擔心的莫過於建置成本；對此，劉建伸坦言，系統本身的確不便宜，從導入初期至今已經投入 8 位數的成本，但後續帶來的效益也相當明顯，像是養殖戶過去都得長時間留守漁塭，在導入系統後，可將平均每天 8 小時的工作時間降低至 6.5 小時。

人力成本也可進一步降低，王靜儀估算，透過導入系統，每 100 公頃養殖面積的人力可從 33 人降低至 10 人，節省近 2,000 萬的人力成本。

(大學剛畢業) 找工作或推甄研究所所需的履歷表和自傳

(一頁的) 履歷表 (註 1)：包括 (如果某項沒有，則不用寫)

• 個人基本資料和聯絡方式：例如電話、email、(專業的) blog 等等。
• 學歷：大學以上的部份。
• 成績：如果前幾名，可以列班級或系排名，學校成績單通常有附帶說明。某同學是全系第一名，卻沒有說明之；找工作時，謙虛不是美德。某同學是全班第二名，卻說前幾名；別人心中會有疑惑，是第 10 名？如果成績不理想，但主修比較高，可以單獨列出；或者說明逐年進步的情況。

Learning an Inventory Control Policy with General Inventory Arrival Dynamics

S Andaz, C Eisenach, D Madeka, K Torkkola, R Jia, D Foster, S Kakade, Learning an Inventory Control Policy with General Inventory Arrival Dynamics, 2023, arXiv preprint arXiv:2310.17168. (Amazon)

In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arrivals model (QOT). We also allow for order quantities to be modified as a post-processing step to meet vendor constraints such as order minimum and batch size constraints -- a common practice in real supply chains. To the best of our knowledge this is the first work to handle either arbitrary arrival dynamics or an arbitrary downstream post-processing of order quantities. Building upon recent work (Madeka et al., 2022) we similarly formulate the periodic review inventory control problem as an exogenous decision process, where most of the state is outside the control of the agent. Madeka et al. (2022) show how to construct a simulator that replays historic data to solve this class of problem. In our case, we incorporate a deep generative model for the arrivals process as part of the history replay. By formulating the problem as an exogenous decision process, we can apply results from Madeka et al. (2022) to obtain a reduction to supervised learning. Finally, we show via simulation studies that this approach yields statistically significant improvements in profitability over production baselines. Using data from an ongoing real-world A/B test, we show that Gen-QOT generalizes well to off-policy data.