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
- 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
- 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