一下時，我的計程補考 (blog)。當時老師的教學方式是板書上課後給作業；對初學者而言，困難的是程式的邏輯 (if/for) 和除錯 (debug)，邏輯可以借由上課中的例子說明之 (blog)，但是除錯肯定是需要幫忙的。國內外許多課程都有類似的設計，例如元智資管的程設、交大應數的高等微積分，連頂尖的 MIT 都有 (課程, 說明)，更何況是一般的學生。
11/30/2020
11/28/2020
Does Advertising Actually Work?
Freakonomics, 441. Does Advertising Actually Work? (Part 2: Digital) (Part 1: TV)
行銷的關鍵是要確定因果和相關，例如某大零售商一年只有在三個重要節日做廣告，銷售量的增加是因為季節效應、還是廣告？在這個有趣的節目裡面，和 eBay 合作的經濟學家，做了一個 A/B 測試實驗，說明其搜尋引擎關鍵字廣告效果有限。當時的 eBay 總經理因此決定縮減一億美元的線上廣告預算。
T. Blake, C. Nosko, and S. Tadelis, Consumer heterogeneity and paid search effectiveness: A large‐scale field experiment, Econometrica, 83 (1), pp. 155-174, 2015.
Our experiments show that the e ffectiveness of SEM is small for a well-known company like eBay and that the channel has been ine ffective on average....
We then use our experimental methods that control for endogeneity to find a ROI of -63%, with a 95% confidence interval of [-124%; -3%], rejecting the hypothesis that the channel yields positive returns at all.
11/27/2020
專題實作和金門行銷
這學期上一門大三的專案實作，今天談到以考試為主的傳統教學缺點，我舉兩個例子說明。朋友的小孩去年唸小二，學校教月的陰晴圓缺，竟然是用背誦的，小朋友痛苦不堪。朋友是工程碩士，透過兩個球體和觀察，了解月形狀的變化，輕輕鬆鬆就記下來了。令人驚訝的不是背不下來的小孩，而是班上九成的同學硬背下來，這才是教育的根本問題。更深層的原因是我們的師資培育方式，我們現在是包班制，這是制度造成的問題；數理比較抽象，在芬蘭需要具備有(理工) 碩士才能任教。
11/26/2020
矽谷創業之神陳五福的創投心法練成術
連以婷 ，「坦白說，我開始走創投是失敗的！」矽谷創業之神陳五福的創投心法練成術，科技新報，2020 年 02 月 28 日
坦白說我開始走創投是失敗的，因為做投資時還是依循過去當創業家的心態，但這兩者其實很不一樣。創業家要非常樂觀，因為每個人都知道創業成功的機率可能十之一二，如果不保持樂觀的心態、不相信現在做的事情會成功，別說做下去連創業都不敢想。
但做創投的人則要倒過來，要非常的謹慎、要抽絲剝繭找出所有可能的失敗風險，與創業家互相制衡並補足他們不懂的地方，提醒他們需要小心的部分。我一開始做創投就過於同理這些創業家，好像把過去那份創業的激情投射在他們身上，於是就跟著他們樂觀，反而缺少了制衡力。...
11/25/2020
Uber 應對失誤的 A/B 測試
地球圖輯隊，道歉也有公式可學？他教會Uber如何應對失誤，簡單一招比千百句對不起更有效，2020.11.16
當天晚上，身為Uber首席經濟學家的里斯特立刻打電話給當時的Uber執行長卡拉尼克（Travis Kalanick），劈頭直說：「這趟旅程爛透了，我再也不會用你的APP了。而且最糟糕的是，我連一句道歉都沒收到。」
11/23/2020
The BAIR Blog
The Berkeley Artificial Intelligence Research (BAIR) blog
The BAIR Blog provides an accessible, general-audience medium for BAIR researchers to communicate research findings, perspectives on the field, and various updates. Posts are written by students, post-docs, and faculty in BAIR, and are intended to provide relevant and timely discussion of research findings and results, both to experts and the general audience. Posts on a variety of topics studied at BAIR will appear approximately once every two weeks.
They could explain the technical details in a nice and amazingly clear way, so I really enjoy their writing. Taking this blog as an example, they use two-way consistency and its corresponding picture to explain sparse graphical memory for robust planning.
11/21/2020
The Stoic Challenge (堅忍的挑戰)
William B. Irvine, The Stoic Challenge: A Philosopher's Guide to Becoming Tougher, Calmer, and More Resilient, W. W. Norton & Company, 2019.
A practical, refreshingly optimistic guide that uses centuries-old wisdom to help us better cope with the stresses of modern living.
11/19/2020
Researchers develop machine-learning optimizer to slash product design costs
Brett Hansard, Researchers develop machine-learning optimizer to slash product design costs, Argonne National Laboratory, NOVEMBER 16, 2020.
Speed up the product design optimization process:
It employs a novel machine learning technique that helps users focus on how to most efficiently target computational resources. (Machine learning is an application of artificial intelligence that allows systems to automatically learn and improve from experience.)
"ActivO runs the simulations in a very smart way and quickly identifies the parts of the design space we should focus on," explained Pal. "A process that used to take two to three months to give you the optimum design can now be completed within about a week."
11/18/2020
11/17/2020
馬大康：未來AI技術將有7大挑戰與新機會
余至浩，新任Google臺灣董事總經理馬大康：現階段全球AI發展仍處於石器時代，未來AI技術將有7大挑戰與新機會，iThome，2020-11-15
但講到現今AI的發展，馬大康仍以AI石器時代來加以形容，現階段全球AI發展仍在起步階段，未來還有很大成長空間。他並歸納出7項未來AI技術挑戰與新機會。
11/06/2020
躁動的帝國
林添貴譯，躁動的帝國：從清帝國的普世主義，到中國的民族主義，一部250年的中國對外關係史，八旗文化，2020
Odd Arne Westad, Restless Empire: China and the World since 1750, Basic Books, 2015.
談中國近代史，因為國共兩黨、不同族群，產生了不一樣的史觀。作者是挪威的歷史學家，曾在北京大學和清華大學教學，從國際局勢和各方角力，提供了不同的視野，值得深讀，以增加我們看事情的廣度。24 頁的註釋資料，引用許多重要政治人物所說的話，和官方歷史教科書的說法有出入。
在這一場巨大的轉變中，除了檯面上的政治人物外，書中也道盡一般老百姓的無奈、痛苦、和生離死別。
11/05/2020
學習數學的四個層次：(4) 純粹滿足好奇心或求知慾
學習數學的四個層次：(0) 如何學數學，(1) 代表具備基礎的知識與能力，(2) 邏輯推理和抽象思考的能力，(3) 在許多行業的應用，(4) 純粹滿足好奇心或求知慾。
2015/12/1 初稿，持續更新中。
一般性說明
2015/12/1 初稿，持續更新中。
一般性說明
- 因為內在的動力與好奇心，許多數學家 (和許多研究人員一樣) 終其一身，都在解決一些未知的問題。
- 許多看似沒用的數學理論，後來卻發現有許多有趣且重要的應用。
11/04/2020
Deep Learning: An Introduction for Applied Mathematicians
Catherine F. Higham and Desmond J. Higham, Deep Learning: An Introduction for Applied Mathematicians, SIAM Review, 2019, Vol. 61, No. 4, pp. 860–891.
11/03/2020
A Nonparametric Approach to Modeling Choice with Limited Data
Vivek F. Farias, Srikanth Jagabathula, and Devavrat Shah, A Nonparametric Approach to Modeling Choice with Limited Data, Management Science, February 2013, Vol. 59, No. 2, pp. 305-322.
Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the “right” model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a “generic” model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward “automating” the crucial task of choice model selection.
The authors formulated the minimum revenue problem under consumer choices as a linear programming with exponential growing of decision variables in terms of product number. Based on duality, they developed polynomial-time algorithms by using constraint sampling and efficient representation of purchase permutations. Profs. Farias and Shah then founded the company Celect and was later acquired by Nike. Once again, it demonstrates the positive cycle of advanced research and academic-industrial collaboration.