Guides for students in Business Analytics Laboratory (商業分析實驗室學生指引)

Knowledge to master for a better foundation (and future)

Tools and general: 

• 李琳山老師，碩博士生應該要知道的事情  (YouTube)

• David A. Patterson, Your Students Are Your Legacy, Communications of the ACM, March 1, 2009, Vol. 52, No. 3, Pages: 30-33. (Non-Technical Talks)
• Helpful guidance for students:
• Show initiative, for fortune favors the bold. Don’t wait for professors to tell you what to do; if we were good managers, we probably wouldn’t be faculty. Explore, challenge assumptions, and don’t let lots of prior art discourage you.
• Sink or swim. We’ll offer you what we think are great projects with plenty of potential, and we’ll support you the best we can, but it’s what you do with the opportunity that makes or breaks your graduate student career.
• Educate your professor. We’re in a fast-moving field, so for us to give you good advice we need to know what you’re working on. Teach us!
• I searched and read papers related to my research and presented them to my master's thesis advisor during our regular meetings even though he did not request me to do it. 
• Jonathan Wu, AI論文工具 | 如何一天看3~5篇論文？寫論文軟體推薦 | 論文工具 | AI 工具 EP4

My lab:

• Application domains: Manufacturing, service industries (with appropriate domain knowledge by taking courses and reading articles/books) 
• Main tools: Machine learning, mathematics, operations research, Python (or Julia), probability and statistics
• Nice quality to have: Enjoy learning and coding (or willing to), think independently, willing to share and help
• More information: How to do research, presentation, English writing ...
• S. Keshav, How to Read a Paper, CS, University of Cambridge. (influential, 2 pages only)
• To-to list once you join the lab: Dropbox, Python 
• How to improve your English (pdf, new)

Find nice resources with Python codes and review by using Google search: 

• Papers with code
• topic + python site:arxiv.org
• topic + python github
• topic + pypi
• topic + kaggle
• Datasets
• Journal of Machine Learning Research: Many papers come with code.
• Open Review: Nice to understand the different perspectives of a research article (new)

Python for everyone:

• Python 程式設計
• 運算思維與程式設計 (6.1 追蹤和除錯)
• Please use Spyder to trace and debug a program.
• fundamental: if, for/while, function, object
• data structure: list, dictionary, set, tuple
• data manipulation: NumPy, Pandas, matplotlib, SciPy
• John Guttag, Introduction to Computation and Programming Using Python: With Application to Understanding Data (6.0001, 6.0002, edX) (the best)
• Robert Johansson, Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Apress. (CYCU eBook)
• Kaggle, Gain the skills you need to do independent data science projects. (new)
• David J. Malan, CS50's Introduction to Programming with Python, Harvard (Amazing)
• Wes McKinney, Python for Data Analysis, O'Reilly, third edition, 2022. (Open access, tools for data manupulation and the best one)
• Al Sweigart, Automate the Boring Stuff with Python. (nice introduction)
• 1500 most common data structures and algorithms solutions
• 學習程式設計的心法與方法
• data structure algorithm python coursera: UC at Boulder (new student, learn it during the first summer or winter vacation)

Software engineering:

• Mariano Anaya, Clean Code in Python, Second Edition, Packt Publishing, 2018. (Accesible with CYCU account) 
• Frederick Brooks Jr., The Mythical Man-Month: Essays on Software Engineering, Addison-Wesley,  1995. (The classic, CYCU book)
• Ashwin Pajankar, Python Unit Test Automation: Automate, Organize, and Execute Unit Tests in Python, Springer, 2022.  (Accesible with CYCU account) 
• Anand Balachandran Pillai, Software Architecture with Python, Packt Publishing, 2017. (CYCU ebook)
• John Sonmez, Soft Skills: The software developer's life manual, Manning Publications, 2014.
• Titus Winters, Tom Manshreck, and Hyrum Wright, Software Engineering at Google, O'Reilly Media, March 2020. (Read online)

For master students:

• 學習數學的四個層次
• Fall: IE179R 實驗設計，ME733L RL 增強式學習在智慧製造的實作應用 
• (大學部) IC258D 資料結構 (查怡老師，Python)，CS361L 資料庫系統
• IE623R 機器學習，IE412R 人工智慧
• EL602R 計算機演算法，EL407R 最佳化理論 (楊緒文老師)
• Spring: IE767R 智慧型製造系統，IE151r 多變量分析
• choose 1 or 2: CS361R 資料庫系統 (Database system)，EL602R 計算機演算法, IE605R 物件導向分析與設計 (Object-Oriented Analysis and Design)
• choose 1 (BA595R 企業資源排程與應用，IE812r 生產作業決策管理，IE694R 存貨系統，IE380r 網路分析)
• Further information: To master the (online) course material, you need to spend enough time finishing all readings and assignments. 
• Bachir El Khadir, Visually Explained (Machine Learning and Optimization videos with a strong emphasis on building intuition with visual explanations.) (new)
• OR and statistics/math related: The more, the merrier. 
• Mathematics for Machine Learning Specialization, Coursera
• Mathematics for Machine Learning and Data Science, Coursera (the best if you do not have background in these courses)
• Mathematics for Machine Learning and Data Science Specialization,  deeplearning.ai
• 3Blue1Brown, Amazing videos on math, neural network
• Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, Mathematics for Machine Learning (pdf)
• D. Bertsimas, The Analytics Edge, edX. (The best entry course for machine learning and optimization.)
• Computer science: Algorithm (演算法), data structure, database
• 培養英文聽力的方法

International students: 

• Fall: IE412R Artificial Intelligence, IE623R Machine Learning, IE794R Supply Chain Management
• Spring: EL617R Mathematical Methods in Signal Processing, CS456R Nature Language Processing

For Ph.D. students: 

• First year: 3 courses at most for each semester
• After the second year: 2 courses at most for each semester
• P.R. Kumar, On graduate studies and research, UIUC, 2009. 
• Take a solid set of foundational courses. 
• There is no substitute for theoretical depth.
• Go to the original classics. (Some papers at the end of this page)
• Online courses to take: Please work on the problem sets that are important for mastering the material.
• Dr. Casey Rodriguez, MIT 18.100A Real Analysis, Fall 2020, MIT OpenCourseWare. 
• It is an excellent way to enhance your mathematical reasoning and maturity which is important for doing research and lifelong learning.
• As a graduate student, I took 3 year-long courses (under) real analysis, (graduate) real analysis, and functional analysis in the math department.   
• S. Boyd, Convex Optimization, Stanford University. 
• Optimization methods are eveywhere, e.g., supply chain, machine learning, engineering.
• Constantine Caramanis, Optimization Algorithms, Combinatorial Optimization, UT Austin. (new)
• Robert Gallager, MIT 6.262 Discrete Stochastic Processes, Spring 2011. (new)
• Once you master this fundamental knowledge, you can learn anything by yourself in the future. 

Conferences:

• APIEMS: Submission Deadline of Abstract June 16th,  Conference Date : Oct 22nd – 26th (varied)
• 中國工業工程學會，年會暨學術研討會，徵稿日期 07.01- 09.19
• 台灣作業研究學會年會暨學術研討會，截稿日期：10.15

Competitions:

• Under 
• 中原大學工業與系統工程學系，專題實作競賽，報名時間：3.14 - 4.08
• 中國工程師學會學生分會，大專學生工程論文競賽：4 月 28 日
• 中國工業工程學會，「工業工程與管理」大學生專題論文競賽：112 年 4 月 28 日 
• 台灣作業研究學會，大專校院專題競賽，網路報名日期：5月01日至5月31日
• 大專校院資訊應用服務創新競賽，報名日期：9月26日~ 10月6日
• 中原大學電機資訊學院，原力覺醒-專題暨 AI 創意構想競賽，分為「專題製作組」及「創意構想組，截止 12/21
• Graduate 
• 中國工業工程學會，「工業工程與管理」 碩士論文競賽： 6 月 4 日
• 富邦人壽管理博碩士論文獎，5/13 - 7/16
• 台灣作業研究學會，碩博士論文競賽，截稿日期 9 月 16 日前

Interesting readings (You could follow your favorite authors under their Google Scholar Profiles to know their newest research articles.) 

• Decision tree 
• D. Bertsimas and J. Dunn, Optimal classification trees, Machine Learning, 2017. 
• L Breiman, Bagging predictors,  Machine Learning, 1996, 24(2), 123-140.
• L. Breiman, Random forests, Machine Learning, 2001, 45(1), 5-32.
• Tianqi Chen and Carlos Guestrin, XGBoost: A Scalable Tree Boosting System, KDD '16, Pages 785–794 
• J.H. Friedman, Greedy function approximation: A gradient boosting machine, Annals of Statistics, 2001, 29(5), 1189-1232.
• D. Bertsimas and M. Sim, The price of robustness, Operations Research, 2004, 52(1), 35-53.
• D. Bertsimas, A. King, and R. Mazumder, Best subset selection via a modern optimization lens, The Annals of Statistics, 2016, 44(2), 813–852. (A collection of papers for further comparisons)
• P. Domingos, A Few Useful Things to Know About Machine Learning, Communications of the ACM, 2012, 55(10), 78-87. (Broad introduction)
• Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative adversarial networks, Advances in neural information processing systems, 2014. 
• A. Krizhevsky, I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Communications of the ACM, 2017, 60(6), 84-90.
• Y. LeCun, Y. Bengio, and G.E. Hinton, Deep learning, Nature, 2015, 521(7553), 436-444. (Broad introduction)
• D. Silver, A. Huang, and C. Maddison et al. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529, 484–489.
• Robert Tibshirani, Regression Shrinkage and Selection Via the Lasso, Journal of the Royal Statistical Society: Series B (Methodological), 1996, 58(1), 267–288.
• Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin, Attention is All you Need, Advances in Neural Information Processing Systems 30, NeuIPS 2017. 
• Pieter Abbeel: The one that's essentially overtaken all of AI in the last five plus years and trend seems to just continue
• Many related and fun books to read