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

Customer Choice Models vs. Machine Learning

Jacob Feldman, Dennis J. Zhang, Xiaofei Liu, and Nannan Zhang (2021) Customer Choice Models vs. Machine Learning: Finding Optimal Product Displays on Alibaba. Operations Research 70(1):309-328. (Best OM Paper in Operations Research Award: Finalist, pdf, implementation details)

Optimization in Online Content Recommendation Services

Omar Besbes, Yonatan Gur, Assaf Zeevi, Optimization in Online Content Recommendation Services: Beyond Click-Through Rates, 18(1), pp. 15–33, Manufacturing & Service Operations Management, Volume 18, Issue 1, Winter 2016. 

A new class of online services allows Internet media sites to direct users from articles they are currently reading to other content they may be interested in. This process creates a “browsing path” along which there is potential for repeated interaction between the user and the provider, giving rise to a dynamic optimization problem. A key metric that often underlies this recommendation process is the click-through rate (CTR) of candidate articles. Whereas CTR is a measure of instantaneous click likelihood, we analyze the performance improvement that one may achieve by some lookahead that accounts for the potential future path of users. To that end, by using some data of user path history at major media sites, we introduce and derive a representation of content along two key dimensions: clickability, the likelihood to click to an article when it is recommended; and engageability, the likelihood to click from an article when it hosts a recommendation. We then propose a class of heuristics that leverage both clickability and engageability, and provide theoretical support for favoring such path-focused heuristics over myopic heuristics that focus only on clickability (no lookahead). We conduct a live pilot experiment that measures the performance of a practical proxy of our proposed class, when integrated into the operating system of a worldwide leading provider of content recommendations, allowing us to estimate the aggregate improvement in clicks per visit relative to the CTR-driven current practice. The documented improvement highlights the importance and the practicality of efficiently incorporating the future path of users in real time.

Machine learning and statistical testing at vantagescore

Kimberly Adams, How one credit scoring company is thinking about financial inclusion, Marketplace Tech,  JULY 8, 2022. (more around July 2022)

作業研究的持續改善

下學年要教我喜歡的作業研究。依照往例，再一次更新之前的版本。

第一學期，考慮確定的環境和有限資源下，使用最佳化 (optimization)，以最大化 (例如利潤) 或最小化目標  (例如成本)。第二學期，準備教不確定性環境下，如何分析、最佳化和做決策 (控制)。

Online Network Revenue Management Using Thompson Sampling

Kris Johnson Ferreira, David Simchi-Levi, and He Wang. (2018). “Online network revenue management using Thompson sampling.” Operations Research, 66(6), 1586-1602. (Supplemental Material, code, MSOM Society 2021 Operations Research Best OM Paper Award)

We consider a price-based network revenue management problem in which a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As is common in practice, we assume the demand function contains unknown parameters that must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a trade-off commonly referred to as the “exploration-exploitation trade-off.” Toward the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price (“exploration” objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). We propose a class of dynamic pricing algorithms that builds on the simple, yet powerful, machine learning technique known as “Thompson sampling” to address the challenge of balancing the exploration-exploitation trade-off under the presence of inventory constraints. Our algorithms have both strong theoretical performance guarantees and promising numerical performance results when compared with other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multiarmed bandit problems with resource constraints as well as in applications in other revenue management settings and beyond.

為什麼工 (資) 管的課程看起來很雜？

(2022) 因為橫跨兩個學院 (工和商)，工業系龐雜的內容，和資管系一樣，所以修改一下標題。其實，我的部落格如何選填大學志願，就是以四個專業和李國鼎的話 (第 17 頁)，說明這兩個系的重要性。

(2014) 常常聽到學生有這樣的疑惑，我試著以營收管理說明之；針對固定 (如旅館房間、網頁上廣告空間) 且易過時 (如機位、時裝) 的容量或庫存供給下，如何有效地分配庫存 (屬於生管) 和 (動態) 定價 (屬於行銷)，以最大化企業之營收；詳細的內容可以參考我的課程。

7 real-world applications of reinforcement learning

 Joy Zhang, 7 real-world applications of reinforcement learning, gocoder, February 17, 2022

1. Autonomous driving with Wayve

2. Personalizing your Netflix recommendations

3. Optimizing inventory levels for Walmart

4. Improving search engine results with search.io

5. Improving language models with OpenAI's WebGPT

6. Trading on the financial markets with IBM's DSX platform

7. Robotics with the University of California, Berkeley

台積電的訂價策略

陳良榕，台積電怎麼贏？， 天下雜誌 609 期，2016-10-25

經營人的學習與成長

張忠謀，經營人的學習與成長，台灣玉山科技協會20周年慶祝大會暨論壇-晚宴，10月26日

經營人的學習與成長

張忠謀，經營人的學習與成長，台灣玉山科技協會20周年慶祝大會暨論壇-晚宴，2021年10月26日

Optimization modelling: A practical approach

Ruhul Amin Sarker and Charles S. Newton, Optimization modelling: A practical approach, CRC Press, 2007. 

Although a useful and important tool, the potential of mathematical modelling for decision making is often neglected. Considered an art by many and weird science by some, modelling is not as widely appreciated in problem solving and decision making as perhaps it should be. And although many operations research, management science, and optimization books touch on modelling techniques, the short shrift they usually get in coverage is reflected in their minimal application to problems in the real world. Illustrating the important influence of modelling on the decision making process, Optimization Modelling: A Practical Approach helps you come to grips with a wide range of modelling techniques.

Revenue Management and Pricing Analytics

Guillermo Gallego and Huseyin Topaloglu, Revenue Management and Pricing Analytics, Springer, 2019.

The book is divided into three parts: traditional revenue management, revenue management under customer choice, and pricing analytics. Each part is approximately of the same length and written in a self-contained way, so readers can read them independently, although reading the first part may make the second part easier to understand. Each chapter ends with bibliographical notes where the reader can find the sources of the material covered as well as many useful references. Proofs of some important technical results can be found in the appendix of each chapter. Solving the end-of-chapter problems helps reinforce the material in the book, with some of the questions expanding on the subject....

There is enough material in the book for a full-semester course for advanced undergraduate or master’s students. Parts I and II can be covered in about 9 weeks and Part III in about 4weeks excluding the last two chapters on online learning and competition, which can be assigned as independent readings. 

Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges

Duncan Simester, Artem Timoshenko, and Spyros I. Zoumpoulis, Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges, Management Science, 2020, Vol. 66, No. 6, Pages: 2495–2522.

We investigate how firms can use the results of field experiments to optimize the targeting of promotions when prospecting for new customers. We evaluate seven widely used machine-learning methods using a series of two large-scale field experiments. The first field experiment generates a common pool of training data for each of the seven methods. We then validate the seven optimized policies provided by each method together with uniform benchmark policies in a second field experiment. The findings not only compare the performance of the targeting methods, but also demonstrate how well the methods address common data challenges. Our results reveal that when the training data are ideal, model-driven methods perform better than distance-driven methods and classification methods. However, the performance advantage vanishes in the presence of challenges that affect the quality of the training data, including the extent to which the training data captures details of the implementation setting. The challenges we study are covariate shift, concept shift, information loss through aggregation, and imbalanced data. Intuitively, the model-driven methods make better use of the information available in the training data, but the performance of these methods is more sensitive to deterioration in the quality of this information. The classification methods we tested performed relatively poorly. We explain the poor performance of the classification methods in our setting and describe how the performance of these methods could be improved.

Efficient Large-Scale Internet Media Selection Optimization for Online Display Advertising

Courtney Paulson, Lan Luo, and Gareth M. James, “Efficient Large-ScaleInternet Media Selection Optimization for Online Display Advertising,” Journal of Marketing Research, vol. 55, 2018, pp. 489-506. 

In today's digital market, the number of websites available for advertising has ballooned into the millions. Consequently, firms often turn to ad agencies and demand-side platforms (DSPs) to decide how to allocate their Internet display advertising budgets. Nevertheless, most extant DSP algorithms are rule-based and strictly proprietary. This article is among the first efforts in marketing to develop a nonproprietary algorithm for optimal budget allocation of Internet display ads within the context of programmatic advertising. Unlike many DSP algorithms that treat each ad impression independently, this method explicitly accounts for viewership correlations across websites. Consequently, campaign managers can make optimal bidding decisions over the entire set of advertising opportunities. More importantly, they can avoid overbidding for impressions from high-cost publishers, unless such sites reach an otherwise unreachable audience. The proposed method can also be used as a budget-setting tool, because it readily provides optimal bidding guidelines for a range of campaign budgets. Finally, this method can accommodate several practical considerations including consumer targeting, target frequency of ad exposure, and mandatory media coverage to matched content websites.

Algorithm: Coordinate descent algorithm for budget optimization problem (7).

Dataflow-as-a-Service of SambaNova

Inside AI, 2021/4/14

SambaNova, a developer of AI hardware and software systems, raised $676M in financing that values the startup at over $5.1B. The Nvidia competitor makes chips for AI processes, which it uses to build servers and AI software that it leases to other businesses.

Charting a business course for reinforcement learning

Jacomo Corbo, Oliver Fleming, and Nicolas Hohn, It’s time for businesses to chart a course for reinforcement learning, McKinsey, April 1, 2021.

Broadly speaking, we see reinforcement learning delivering this value across the business, with potential applications in every business domain and industry (Exhibit 2). Some of the near-term applications for reinforcement learning fall into three categories: speeding design and product development, optimizing complex operations, and guiding customer interactions.

 Exhibit 2 some applications.

To be sure, implementing reinforcement learning is a challenging technical pursuit. A successful reinforcement learning system today requires, in simple terms, three ingredients:

1. A well-designed learning algorithm with a reward function. 
2. A learning environment.  
3. Compute power. 

Computing power is better than compute power. 

Feedback Control in Programmatic Advertising

N. Karlsson, Feedback Control in Programmatic Advertising: The Frontier of Optimization in Real-Time Bidding, IEEE Control Systems Magazine, vol. 40, no. 5, pp. 40-77, Oct. 2020.

Feedback control is critical in the scalable optimization of Internet advertising, and it is, therefore, an enabling technology. However, it is challenging to model the plant and design the controller because the plant is nonlinear, time varying, stochastic, and poorly known. A closed-loop system model easily becomes unrealistic or extremely complicated and intractable to analyze.

How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Ads

Ernest Wang, How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Ads, Aug 20, 2020.

People come to Pinterest in an exploration mindset, often engaging with ads the same way they do with organic Pins. Within ads our mission is to help Pinners go from inspiration to action by introducing them to the compelling products and services that advertisers have to offer. A core component of the ads marketplace is predicting engagement of Pinners based on the ads we show them. In addition to click prediction, we look at how likely a user is to save or hide an ad. We make these predictions for different types of ad formats (image, video, carousel) and in context of the user (e.g., browsing the home feed, performing a search, or looking at a specific Pin.)

咖啡廳裡親子的對話

 有一天，在咖啡廳裡工作，聽到隔壁一對父母在教小孩。

大概的情況是，小孩子的作業訂正有問題，媽媽非常地生氣，期間也說明別人是如何如何，為什麼你都做不好？小孩子低聲啜泣，對媽媽的話，也不太敢反駁。爸爸則是說，我們不是和乞丐比較，唸書是要幫你找到好工作、賺錢 。後來，我和小孩剛好都離座，在走道上，我仔細地看了小孩一眼，一看就是一個乖巧、懂事的小孩子。回座後，夫妻開始有不同的意見。前後超過一個小時。

當下有非常高的衝動，想要和父母講一下。但是，我怕被白眼，所以還是忍了下來。