Algorithmic High-Dimensional Robust Statistics
Algorithms for Reinforcement Learning
An Introduction to Formal Languages and Automata
An Introduction to Graph Theory
An Introduction to Quantum Computing
An Introduction to Statistical Learning
An Owner's Guide to the Human Genome
Annotated Algorithms in Python
Artificial Intelligence: Foundations of Computational Agents
Bayesian models of perception and action
A Business Analyst’s Introduction to Business Analytics
Competitive Programmer's Handbook
Computational and Inferential Thinking
Computational Discovery on Jupyter
Computational Topology for Data Analysis
Computer Vision: Algorithms and Applications
Data Science and Machine Learning
Data Structures and Information Retrieval in Python
Differential Equations (Chasnov)
Distributional Reinforcement Learning
Earth Engine User Guide: Python
Elements of Applied Functional Analysis
Feedback Systems: An Introduction for Scientists and Engineers
Forecasting: Principles and Practice
Fundamentals of Numerical Computation
Geospatial Data Science with Leafmap
Hands-On Machine Learning with R
How to Think Like a Computer Scientist
Human-Robot Interaction — An Introduction
Information Theory: From Coding to Learning
Interpretable Machine Learning
Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
Introduction to Automata Theory, Languages, and Computation
Introduction to Autonomous Robots
Introduction to Causal Inference
Introduction to Classical and Quantum Computing
Introduction to Control Systems
Introduction to Differential Geometry
Introduction to Financial Engineering with R
Introduction to Information Theory
Introduction to Linear Algebra
Introduction to Modern Cryptography
Introduction to Partial Differential Equations
Introduction to Probability, Statistics, and Random Processes
Introduction to Smooth Manifolds
Introduction to Modern Statistics
Introduction to Theoretical Computer Science
Introduction to the Theory of Computation
Learning Theory and Kernel Machines
Lecture Notes on Financial Mathematics
Machine Learning - A First Course for Engineers and Scientists
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
Mathematics for Machine Learning
Mechanical Engineering Methods
Mixtape: Causal Inference for Statistics
Modeling and Simulation in Python
Modern Data Science with R (3nd Edition)
Modern Statistics for Modern Biology
Multi-Agent Reinforcement Learning
Natural Language Processing with Python
Notes on Continuous Optimization
Numerical methods for partial differential equations
Numerical Methods for Scientists and Engineers
Partial Differential Equations
Pattern Recognition and Machine Learning
Probabilistic Machine Learning Book 1
Probabilistic Machine Learning Book
Probability Theory: The Logic of Science
Problem Solving with Algorithms and Data Structures using Python
Qualitative Dynamics and Chaos
Quantitative Economics with Python
Quantum Computing for Computer Scientists
Reinforcement Learning: An Introduction
Reinforcement Learning for Finance
Speech and Language Processing
Spatial Statistics for Data Science
Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling
Technical Analysis with R (Second Edition)
The Effect: An Introduction to Research Design