1 Broad Literature

Causal Inference and Its Applications in Online Industry(Deng, n.d.)


High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications


Full Stack Deep Learning 2022

Regression and Other Stories (Gelman, Hill, and Vehtari 2020)

Applied Bayesian Modelling (Congdon 2014)

Causal Inference The Mixtape (Cunningham 2021)

Introduction to the concept of likelihood and its applications (Etz 2017)

Introduction to Probability for Data Science (IntroductionProbabilityData?)

A Review of Generalizability and Transportability (Degtiar and Rose 2023)

Regression and Causality (Schomaker 2021)

What are the most important statistical ideas of the past 50 years? (Gelman and Vehtari 2021)


The Effect: An Introduction to Research Design and Causality (huntington-kleinEffectIntroductionResearch?)

Natural Language Processing Advancements By Deep Learning: A Survey (Torfi et al. 2021)

Minimum Viable Study Plan for Machine Learning Interviews (Pham 2022)

https://www.bradyneal.com/causal-inference-course Introduction to Causal Inference (Neal 2020)

What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory (Lundberg, Johnson, and Stewart 2021)


ML use cases by company


The Ultimate Guide to Machine Learning Job Interviews


Introducing the Facebook Field Guide to Machine Learning video series

Advice On Interviewing With Amazon

Do you see companies asking to implement algorithm from scratch? Yes, some companies like LinkedIn, Intuit did. The common questions include: implement kmeans, linear/logistic regression. You can find the code here. backprop https://github.com/khangich/machine-learning-interview/blob/master/sample/backprop.py kmeans https://github.com/khangich/machine-learning-interview/blob/master/sample/kmeans.ipynb logit https://github.com/khangich/machine-learning-interview/blob/master/sample/logistic_regression.ipynb


Machine Learning engineer onsite interview: one week checklist

(Pearl and Mackenzie 2018) The Book of Why: The New Science of Cause and Effecthttp://bayes.cs.ucla.edu/WHY/jmde-why-review2018.pdf

(“Computational Linear Algebra - YouTube,” n.d.) Computational Linear Algebra for Coders(“Fastai/Numerical-Linear-Algebra” 2022)

Which causal inference book you should read A flowchart and a list of short book reviews(Neal 2019)


https://bookdown.org/kevin_davisross/probsim-book/(Ross 2022)

Algorithms & Data Structures Super Study Guide

Pragmatic Social Measurement

Code Tutorials - short annotated coding guides

Hugging Face Tasks


“Computational Linear Algebra - YouTube.” n.d. https://www.youtube.com/playlist?list=PLtmWHNX-gukIc92m1K0P6bIOnZb-mg0hY.
Congdon, Peter. 2014. Applied Bayesian Modelling. John Wiley & Sons.
Cunningham, Scott. 2021. Causal Inference. Yale University Press.
Degtiar, Irina, and Sherri Rose. 2023. “A Review of Generalizability and Transportability.” Annual Review of Statistics and Its Application 10 (1): annurev-statistics-042522-103837. https://doi.org/10.1146/annurev-statistics-042522-103837.
Deng, Alex. n.d. Causal Inference and Its Applications in Online Industry.
Etz, Alexander. 2017. “Introduction to the Concept of Likelihood and Its Applications.” PsyArXiv. https://doi.org/10.31234/osf.io/85ywt.
“Fastai/Numerical-Linear-Algebra.” 2022. fast.ai.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and Other Stories. First. Cambridge University Press. https://doi.org/10.1017/9781139161879.
Gelman, Andrew, and Aki Vehtari. 2021. “What Are the Most Important Statistical Ideas of the Past 50 Years?” arXiv. https://arxiv.org/abs/2012.00174.
Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021. “What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory.” American Sociological Review 86 (3): 532–65. https://doi.org/10.1177/00031224211004187.
Neal, Brady. 2019. “Which Causal Inference Book You Should Read.” https://www.bradyneal.com/which-causal-inference-book.
———. 2020. “Introduction to Causal Inference from a Machine Learning Perspective.” Course Lecture Notes (Draft).
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Penguin Books Limited.
Pham, Khang. 2022. “Minimum Viable Study Plan for Machine Learning Interviews.”
Ross, Kevin. 2022. An Introduction to Probability and Simulation.
Schomaker, Michael. 2021. “Regression and Causality.” arXiv. https://arxiv.org/abs/2006.11754.
Torfi, Amirsina, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavaf, and Edward A. Fox. 2021. “Natural Language Processing Advancements By Deep Learning: A Survey.” arXiv. https://doi.org/10.48550/arXiv.2003.01200.