Introduction
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- Algorithms by Jeff Erickson
- Computer Vision: Algorithms and Applications, 2nd ed. - Richard Szeliski (1st Edition)
- Quantum Computing for the Quantum Curious
- Site Reliability Engineering - Google
- The Little Book of Semaphores - Allen B. Downey
- Software-Defined Networks: A Systems Approach
- Computer Networks: A Systems Approach
- Edge Cloud Operations: A Systems Approach
- Private 5G: A Systems Approach
- TCP Congestion Control: A Systems Approach
- Introduction to Autonomous Robots: Mechanisms, Sensors, Actuators, and Algorithms - Nikolaus Correll, Bradley Hayes, Christoffer Heckman and Alessandro Roncone
- Introduction to Quantum Information Science by Artur Ekert, Timothy Hosgood, Alastair Kay, Chiara Macchiavello (PDF)
- Crafting Interpreters
- Beej's Guide to Network Programming
- Beej's Guide to C Programming
- Programming Languages: Application and Interpretation - Shriram Krishnamurthi
- Introduction to Information Retrieval
- Information Theory - From Coding to Learning
- Algorithmic High-Dimensional Robust Statistics
- Bayesian Data Analysis, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
- Bayesian Modeling and Computation in Python
- Causal Inference - The Mixtape
- Causal Inference: What If - Miguel A. Hern´an, James M. Robins
- Elements of Causal Inference - Peters
- Forecasting: Principles and Practice (3rd ed) by Rob J Hyndman and George Athanasopoulos
- High-Dimensional Probability
- Information Theory: From Coding to Learning by Y. Polyanskiy and Y. Wu
- Introduction to Causal Inference from a Machine Learning Perspective - Brady Neal
- Introduction to Proability 2nd edition - Blitzstein and Hwang
- OpenIntro Statistics
- Prob 140 Textbook - UC Berkeley
- Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- Statistical Thinking for the 21st Century - Russell A. Poldrack
- ALGORITHMS FOR DECISION MAKING
- Bayesian Reasoning and Machine Learning
- Bayesian models of perception and action
- Elements of Statistical Learning
- Computer Age Statistical Inference
- Computational and Inferential Thinking - Ani Adhikari and John DeNero
- Computational Topology for Data Analysis
- Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Dive into Deep Learning
- Eisenstein NLP Notes - Gatech
- High-Dimensional Data Analysis with Low-Dimensional Models, John Wright and Yi Ma
- Information Theory, Inference, and Learning Algorithms
- Introduction to Statistical Learning
- Learning Theory from First Principles by Francis Bach
- Natural Language Processing with Python
- Pattern Recognition and Machine Learning - Christopher Bishop
- Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
- Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy
- Python Data Science Handbook by Jake VanderPlas
- Reinforcement Learning: An Introduction
- Mathematical Foundations of Reinforcement Learning
- Algorithms for Reinforcement Learning - Csaba Szepesvari
- Control Systems and Reinforcement Learning - Sean Meyn - UFL
- Understanding Deep Learning
- Computer Vision: Models, Learning, and Inference
- Interpretable Machine Learning
- Machine Learning in Production: From Models to Products - Christian Kästner, CMU
- Deep Learning for Coders with Fastai & PyTorch
- Physics-based Deep Learning
- Foundations of Data Science
- Understanding Machine Learning: From Theory to Algorithms
- Multimodal Deep Learning
- Deep Reinforcement Learning - Aske Plaat
- Speech and Language Processing - Dan Jurafsky and James H. Martin
- Algebra, Topology, Differential Calculus, and Optimization Theory
- Principles and Practice of Deep Representation Learning
- ALGORITHMS FOR OPTIMIZATION - MYKEL J. KOCHENDERFER AND TIM A. WHEELER
- An introduction to Optimization on smooth manifolds (with book) - EPFL
- Convex Optimization - Boyd
- Engineering Design Optimization by Joaquim Martins and Andrew Ning
- Optimization for Modern Data Analysis - Benjamin Recht and Stephen J. Wright
- Mathematics for Machine Learning
- Linear Algebra - Jim Hefferon
- Introduction to Probability for Data Science - Stanley H. Chan
- An introduction to Optimization on smooth manifolds
- Mathematical Methods in Data Science by Sebastian Roch
- Statistical Machine Learning for Astronomy -- A Textbook - Yuan-Sen Ting
- Machine Learning, Statistics, and Data Mining for Heliophysics - Monica Bobra and James Mason
- Feynman Lectures on Physics - Caltech
- An Introduction to Modern Computational Physics
- Data Analysis for Physics