1 Broad Literature
Causal Inference and Its Applications in Online Industry(Deng, n.d.)
STATISTICAL METHODS FOR RESEARCH WORKERS By Ronald A. Fisher (1925)
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)
https://github.com/khangich/machine-learning-interview/blob/master/extra.md
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
Code Tutorials - short annotated coding guides