2023 Reading List

Another year, another journey through a fascinating set of books. Some of my selections this year were inspired by the excellent Rebel Book Club, a community I’m proud to be part of. Others were part of my ongoing Master’s degree studies in Machine learning. Explore my curated list below!

Books

• An Introduction to Statistical Learning (with Applications in R) (James et al, 2013)
• The Elements of Statistical Learning (Data Mining, Inference, and Prediction) (Hastie et al, 2009)

Audiobooks

• The Coming wave: Technology, Power, and the Twenty-first Century’s Greatest Dilemma (Suleyman, 2023)
• Software Engineering at Google (Winters et al, 2020)
• Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets (Taleb, 2001)
• What Got You Here Won’t Get You There (Goldsmith, 2006)
• Wretched of the Earth (Fanon, 1961)
• The 48 Laws of Power (Greene, 1998)
• Thinking, Fast and Slow (Kahneman, 2011)
• Lost in a Good Game: Why we play video games and what they can do for us (Etchells, 2019)
• Cultish: The Language of Fanaticism (Montell, 2021)
• The Unexpected Joy of the Ordinary (Gray, 2020)
• The Evolution of Desire (Buss, 1994)

Papers

• Fred S. Guthery and Ralph L. Bingham. (2007). A Primer on Interpreting Regression Models. Journal of Wildlife Management 71(3), 684-692, (1 May 2007).
• Amy Berrington de González. D. R. Cox. (2007). Interpretation of interaction: A review. Ann. Appl. Stat. 1 (2) 371 - 385, December 2007.
• Chesnaye, N. C., et. al. (2022). An introduction to inverse probability of treatment weighting in observational research. Clinical Kidney Journal, Volume 15, Issue 1, January 2022, Pages 14–20.
• Kohavi, R., Tang, D., & Xu, Y. (2020). Ethics in Controlled Experiments. In Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing (pp. 116-124). Cambridge: Cambridge University Press.
• Van Der Bles, A. M., et al. (2019). Communicating uncertainty about facts, numbers and science. R. Soc. Open Sci. 6: 181870.
• Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10), 1–23.
• Wickham, H. (2010). A Layered Grammar of Graphics. Journal of Computational and Graphical Statistics, 19(1), 3–28.
• Watson, J., & Holmes, C. (2016). Approximate Models and Robust Decisions. Statistical Science, 31(4), 465–489.
• Meng, X.-L. (2018). Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election. The Annals of Applied Statistics, 12(2), 685–726.
• Lindley, D. V., & Phillips, L. D. (1976). Inference for a Bernoulli Process (a Bayesian View). The American Statistician, 30(3), 112–119.
• Paleyes, A., Urma, R. G., & Lawrence, N. D. (2022). Challenges in deploying machine learning: a survey of case studies. ACM computing surveys, 55(6), 1-29.
• Kim, M., Zimmermann, T., DeLine, R., & Begel, A. (2017). Data Scientists in Software Teams: State of the Art and Challenges. IEEE Transactions on Software Engineering, 44(11), 1024-1038.
• Verma, S., & Rubin, J. (2018). Fairness Definitions Explained. In Proceedings of the international workshop on software fairness (pp. 1-7).