Books and arXiv review papers

(Key 📙 pdf file 🌍 html book)

Optimization

📙 "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe

Machine learning

Books

📙 "An Introduction to Statistical Learning with Applications in Python" by James, Witten, Hastie, Tibshirani, y Taylor
📙 "The Elements of Statistical Learning" by Hastie, Tibshirani y Friedman
📙 "Computer Age Statistical Inference: Algorithms, Evidence and Data Science" by Bradley Efron and Trevor Hastie
📙 "Pattern Recognition and Machine Learning" by Christopher M. Bishop
📙 "Probabilistic Machine Learning: An Introduction" by Kevin Patrick Murphy
📙 "Probabilistic Machine Learning: Advanced Topics" by Kevin Patrick Murphy
📙 "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David
📙 "Foundations of Machine Learning" by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
📙 "Gaussian Processes for Machine Learning" by Carl Edward Rasmussen and Christopher K. I. Williams
📙 "Information Theory, Inference, and Learning Algorithms" by David J. C. MacKay
📙 "Mathematical Analysis of Machine Learning Algorithms" by Tong Zhang
📙 "A Comprehensive Guide to Machine Learning" by Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, Jennifer Listgarten, and Anant Sahai
📙 "A Course in Machine Learning" by Hal Daumé III
📙 "Machine Learning - A First Course for Engineers and Scientists" by Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön
📙 "Automated Machine Learning: Methods, Systems, Challenges" by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren
📙 "Statistics and Machine Learning in Python" by Edouard Duchesnay, Tommy Löfstedt, and Feki Younes
📙 "Bayesian Reasoning and Machine Learning" by David Barber
📙 "Boosting: Foundations and Algorithms" by Robert E. Schapire, and Yoav Freund
📙 "Algorithms for Decision Making" by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray
📙 "Introduction to Algorithmic Marketing" by Ilya Katsov
📙 "Applied Data Science" by Ian Langmore, and Daniel Krasner
📙 "CS229 Lecture Notes" by Andrew Ng, and Tengyu Ma
📙 "Random Matrix Methods for Machine Learning" by Romain Couillet, and Zhenyu Liao
🌍 "Python Data Science Handbook" by Jake VanderPlas
🌍 "A Guide on Data Analysis" by Mike Nguyen
🌍 "Applied Machine Learning for Tabular Data" by Max Kuhn, and Kjell Johnson

arXiv

📙 "A Brief Introduction to Machine Learning for Engineers" by Osvaldo Simeone
📙 "Machine Learning: The Basics" by Alexander Jung
📙 "Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning" by Sebastian Raschka
📙 "Hyper-Parameter Optimization: A Review of Algorithms and Applications" by Tong Yu and Hong Zhu
📙 "How to avoid machine learning pitfalls: a guide for academic researchers" by Michael A. Lones
📙 "Online Learning: A Comprehensive Survey" by Steven C. H. Hoi, Doyen Sahoo, Jing Lu and Peilin Zhao

R realted

🌍 "Hands-On Programming with R" by Garrett Grolemund
🌍 "Hands-On Machine Learning with R" by Bradley Boehmke and Brandon Greenwell
🌍 "Linear Algebra for Data Science with examples in R" by Shaina Race Bennett
📙 "Deep R Programming" by Marek Gagolewski
🌍 "Efficient R programming" by Colin Gillespie, and Robin Lovelace
🌍 "R for Data Science" by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
🌍 "Advanced R" by Hadley Wickham
🌍 "The Epidemiologist R Handbook" editor Neale Batra
🌍 "Practical Statistics in Medicine with R" by Konstantinos I. Bougioukas
🌍 "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson
🌍 "Spatial Statistics for Data Science: Theory and Practice with R" by Paula Moraga
🌍 "R Graphics Cookbook" by Winston Chang
📙 "The R Inferno" by Patrick Burns

Deep learning / neural networks

📙 "The Matrix Calculus You Need For Deep Learning" by Terence Parr, and Jeremy Howard
📙 "The Modern Mathematics of Deep Learning" by Julius Berner, Philipp Grohs, Gitta Kutyniok, and Philipp Petersen
📙 "The Principles of Deep Learning Theory" by Daniel A. Roberts, Sho Yaida, and Boris Hanin
📙 "Machine learning with neural networks" by Bernhard Mehlig
📙 "Dive into Deep Learning" by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
📙 "Deep Learning on Graphs" by Yao Ma and Jiliang Tang
📙 "Physics-based Deep Learning" by Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, and Kiwon Um
📙 "Understanding Deep Learning" by Simon J. D. Prince
📙 "The Little Book of Deep Learning" by François Fleuret
📙 "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory" by Arnulf Jentzen, Benno Kuckuck, and Philippe von Wurstemberger
📙 "Theory of Deep Learning" by Sanjeev Arora, et al.
🌍 "Deep Learning: Foundations and Concepts" by Christopher M. Bishop and Hugh Bishop

Reinforcement learning

📙 "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
📙 "Multi-Agent Reinforcement Learning" by Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer
📙 "Distributional Reinforcement Learning" by Marc G. Bellemare, Will Dabney, and Mark Rowland

Recommender systems

📙 "Recommender Systems" by Linyuan Lü, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou

Anomaly detection

🌍 "That’s weird! Anomaly detection using R" by Rob J. Hyndman

Computer vision

📙 "Computer Vision: Models, Learning, and Inference" by Simon J. D. Prince

Natural language processing

📙 "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition" by Dan Jurafsky and James H. Martin
📙 "Large Language Models" by Michael R. Douglas
📙 "Graph Neural Networks for Natural Language Processing: A Survey" by Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long
🌍 "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson

Causality

📙 "Causal Machine Learning: A Survey and Open Problems" by Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, and Ricardo Silva
📙 "Recent Developments in Causal Inference and Machine Learning" by Jennie E. Brand, Xiang Zhou, and Yu Xie
📙 "A First Course in Causal Inference" by Peng Ding
📙 "Causal Factor Investing" by Marcos M. López de Prado
📙 "Survey and Evaluation of Causal Discovery Methods for Time Series" by Charles K. Assaad, Emilie Devijver, and Eric Gaussier
🌍 "Applied Causal Inference" by Uday Kamath, Kenneth Graham, and Mitchell Naylor

Conformal prediction

📙 "A Tutorial on Conformal Prediction" by Glenn Shafer and Vladimir Vovk
📙 "Conformal Prediction: a Unified Review of Theory and New Challenges" by Matteo Fontana, Gianluca Zeni and Simone Vantini
📙 "A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification" by Anastasios N. Angelopoulos and Stephen Bates

Feature engineering

🌍 "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson

Time series: Forecasting

🌍 "Forecasting: Principles and Practice" by Rob J Hyndman and George Athanasopoulos
📙 "Forecasting: theory and practice" by Fotios Petropoulos et al.
🌍 "Forecasting: theory and practice" (Online version) Editors: Fotios Petropoulos, Yanfei Kang, and Feng Li
📙 "Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices" by Hansika Hewamalagea, Klaus Ackermannb, and Christoph Bergmeir
📙 "A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting" by Aryan Jadon, Avinash Patil and Shruti Jadon
📙 "Time Series Analysis" by Alexander Aue
🌍 "Demand Forecasting for Executives and Professionals" by Stephan Kolassa, Bahman Rostami-Tabar, and Enno Siemsen

Explainability/interpretability

🌍 "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by Christoph Molnar

_{Note: All arXiv papers are inherently free, and all books have been made freely available by their respective authors/publishers.}

FinancialDiets/Compendium-of-free-ML-reading-resources

Books and arXiv review papers

EDA, visualization, and data cleaning

Mathematics

Statistics

Linear regression

Optimization

Machine learning

Books

arXiv

R realted

Deep learning / neural networks

Reinforcement learning

Recommender systems

Anomaly detection

Computer vision

Natural language processing

Causality

Conformal prediction

Feature engineering

Time series: Forecasting

Explainability/interpretability