Matrix Algebra : Theory, Computations and Applications in Statistics
James E. Gentle
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Data-Driven Science and Engineering : Machine Learning, Dynamical Systems, and Control
Steven L. (University of Washington) Brunton
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Numerical Recipes 3rd Edition : The Art of Scientific Computing
William H. (University of Texas, Austin) Press
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The Nature of Computation
Cristopher (, Santa Fe Institute) Moore
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Computational Geometry : Algorithms and Applications
Mark de Berg
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Garbage Collection : Algorithms for Automatic Dynamic Memory Management
Richard (University of Kent at Canterbury, UK) Jones
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Proofs from THE BOOK
Martin Aigner
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Mathematics of Big Data : Spreadsheets, Databases, Matrices, and Graphs
Jeremy Kepner
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Regression Modeling Strategies : With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
Jr., Frank E. Harrell
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An Intermediate Course in Probability
Allan Gut
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Quantum Computer Science : An Introduction
N. David (Cornell University, New York) Mermin
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Data-Driven Science and Engineering : Machine Learning, Dynamical Systems, and Control
Steven L. (University of Washington) Brunton
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Graph Theory
Reinhard Diestel
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An Introduction to Mathematical Cryptography
Jeffrey Hoffstein
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Computability Theory
S. Barry Cooper
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Type Theory and Formal Proof : An Introduction
Rob (Technische Universiteit Eindhoven, The Netherlands) Nederpelt
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Quantum Information Theory
Mark M. (Louisiana State University) Wilde
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Bandit Algorithms
Tor (University of Alberta) Lattimore
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Topological Data Analysis with Applications
Gunnar (Stanford University, California) Carlsson
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Machine Learning : A First Course for Engineers and Scientists
Andreas Lindholm
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Not with a Bug, But with a Sticker : Attacks on Machine Learning Systems and What To Do About Them
Ram Shankar (University of Washington; Harvard University) Siva Kumar
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Probabilistic Numerics : Computation as Machine Learning
Philipp (Eberhard-Karls-Universitat Tubingen, Germany) Hennig
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An Invitation to Combinatorics
Shahriar (Pomona College, California) Shahriari
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Mathematical Aspects of Deep Learning
Philipp (Universitat Wien, Austria) Grohs
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