Miscellaneous - Yan-Yu Chen

Reading Lists

The reading lists provide tailored recommendations for newcomers eager to quickly understand concepts across different fields. However, it's crucial for readers to acknowledge that these suggestions are subjective and may contain errors.

The lists are structured into separate fields of interest, each divided into three tiers: Beginner, Intermediate, and Advanced, indicating the level of prerequisite knowledge required to grasp the content. Although my expertise mainly lies in machine learning and causal inference, I also have a broad interest in other domains and have explored them to some extent.

Machine Learning

(Introductory) Pattern Recognition and Machine Learning, by Christopher M. Bishop
(Introductory) Probabilistic Machine Learning: An Introduction, by Kevin Patrick Murphy
(Intermediate) Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben David
(Intermediate) Convex Optimization, by Stephen Boyd and Lieven Vandenberghe

Deep Learning and Natural Language Processing

(Introductory) Natural Language Processing with Python–Analyzing Text with the Natural Language Toolkit, 2e, by Steven Bird, Ewan Klein, and Edward Loper
(Introductory) Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, 3e, by Dan Jurafsky and James H. Martin
(Introductory) Dive into Deep Learning, by Aston Zhang, Zack C. Lipton, Mu Li, and Alex J. Smola
(Introductory) Practical Deep Learning for Coders, by Jeremy Howard
(Intermediate) Understanding Deep Learning, by Simon J.D. Prince
(Intermediate) Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
(Intermediate) Deep Learning: Foundations and Concepts, by Christopher Bishop and Hugh Bishop
(Intermediate) Build a Large Language Model (From Scratch), by Sebastian Raschka
(Intermediate) Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Causal Inference

(Introductory) Causal Inference: What If, by Miguel A. Hernán and James M. Robins
(Intermediate) A First Course in Causal Inference, by Peng Ding
(Intermediate) Targeted Learning: Causal Inference for Observational and Experimental Data, by Mark J. van der Laan and Sherri Rose
(Intermediate) Causal Inference for The Brave and True, by Matheus Facre Alves
(Advanced) Causality: Models, Reasoning and Inference, 2e, by Judea Pearl

Time Series

(Introductory) Intorductory Econometrics: A Modern Approach, 6e, by Jeffrey M. Wooldridge
(Intermediate) Time Series Analysis and Its Applications: With R Examples, 3e, by Robert H. Shumway
(Intermediate) Time Series: Theory and Methods, 2e, by Peter J. Brockwell and Richard A. Davis

Survival Analysis

(Introductory) The Statistical Analysis of Failure Time Data, 2e, by John D. Kalbfleisch and Ross L. Prentice
(Intermediate) Analysis of Survival Data with Dependent Censoring: Copula-Based Approaches, by Takeshi Emura and Yi-Hau Chen
(Advanced) Econometric Analysis of Cross Section and Panel Data, 2e, by Jeffrey M. Wooldridge

Reinforcement learning

(Intermediate) Reinforcement Learning: Theory and Algorithms, by Alekh Agarwal, Nan Jiang, Sham M. Kakade, and Wen Sun
(Intermediate) Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto

Web Scraping

(Introductory) Web Scraping with Python: Collecting More Data from the Modern Web, 2e, by Ryan Mitchell

Multivariate Statistics

(Intermediate) A First Course in Multivariate Statistics, by Bernard Flury
(Intermediate) Multivariate Statistics: Exercises and Solutions, by Wolfgang Härdle and Zdeněk Hlávka

High-dimensional Statistics

(Advanced) High-Dimensional Statistics: A Non-Asymptotic Viewpoint, by Martin J. Wainwright
(Advanced) High-Dimensional Probability: An Introduction with Applications in Data Science, by Roman Vershynin

Statistical Inference

Topic: Data science, mathematical statistics, functional data analysis

(Introductory) R for Data Science, 2e, by Garrett Grolemund and Hadley Wickham
(Introductory) The Epidemiologist R Handbook, by Neale Batra
(Introductory) Tidyverse Skills for Data Science, by Carrie Wright, Shannon E. Ellis, Stephanie C. Hicks and Roger D. Peng
(Intermediate) Statistical Inference, 2e., by George Casella and Roger L. Berger
(Intermediate) Theoretical Statistics: Topics for a Core Course, by Robert W. Keener
(Intermediate) Functional Data Analysis with R, by Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, and Erjia Cui
(Advanced) Theory of Point Estimation, 2e, by E.L. Lehmann and George Casella
(Advanced) Testing Statistical Hypotheses, 2e, by E.L. Lehmann
(Advanced) Weak Convergence and Empirical Processes: With Applications to Statistics, by Aad W. Vaart and Jon A. Wellner

Probability

Topic: Measure theory, stochastic processes, stochastic calculus

(Intermediate) Introduction to Probability Models, 11e, by Sheldon M. Ross
(Intermediate) Probability and Random Processes, 3e, by Geoffrey Grimmett and David Stirzaker
(Intermediate) Introduction to Stochastic Calculus with Applications, 2e, by Fima C Klebaner
(Intermediate) Gerber-Shiu Risk Theory, by Andreas E. Kyprianou
(Advanced) Two-Dimensional Random Walk: From Path Counting to Random InterLacements, by Serguei Popov
(Advanced) Brownian Motion, Martingales, and Stochastic Calculus, by Jean-François Le Gall
(Advanced) Stochastic Calculus and Financial Applications, by J. Michael Steele
(Advanced) Fluctuations of Lévy Processes with Applications by Andreas E. Kyprianou