Machine Learning (General):
- Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurelien Geron.
- An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani (1st Edition)
- The Elements of Statistical Learning by Jerome Friedman, Trevor Hastie, and Robert Tibshirani
- Pattern Recognition by Sergios Theodoridis and Konstantinos Koutroumbas
- Pattern Classification by Richard Duda, Peter Hart, and David Stork
- Programming Collective Intelligence by Toby Segaran.
- Classification, Parameter Estimation, and State Estimation by Heijden, Duin, Ridder, and Tax.
- Reinforcement Learning: An Introduction 1st Edition by Richard Sutton and Andrew Barto
- Approximate Dynamic Programming by Warren B. Powell
- Nonlinear Regression with R by by Christian Ritz and Jens Carl Streibig.
- Applied Linear Regression by Sanford Weisberg.
- Residuals and Influence in Regression by R. Dennis Cook and Sanford Weisberg.
- Methods and Applications of Linear Models: Regression and the Analysis of Variance by Ronald Hocking.
- Applied Linear Models with R by Daniel Zelterman.
- Applied Predictive Modeling by Max Kuhn and Kjell Johnson
- Statistical Learning from a Regression Perspective by Richard A. Berk
- Time Series Analysis: Forecasting and Control by George E. P. Box and Gwilym M. Jenkins.
- Statistical Methods for Forecasting by Boyas Abraham and Johannes Ledolter.
- Probability and Statistics: For Engineering and the Sciences by Jay L. Devore
- Basic Statistics: Understanding Conventional Methods and Modern Insights by Rand R. Wilcox.
- Mathematical Statistics and Data Analysis by John A. Rice.
- An Introduction to Mathematical Statistics and Its Applications by Richard J. Larsen and Morris L. Marx.
- Computational Statistics Handbook with Matlab by Wendy Martinez and Angel Martinez.
- Statistics for Engineers and Scientists (5th Edition) by William Navidi.
- Adaptive Signal Processing by Bernard Widrow and Samuel D. Stearns.
- Adaptive Filtering Primer by A. Poularikas and Z. Ramadan.
- Adaptive Filtering Theory by Symon Haykin.
- Principles of Adaptive Filters and Self-learning Systems by Anthony Zaknich.
Classification/Decision Theory/Machine Learning/Statistics (Misc):
- Elementary Statistical Quality Control by John T. Burr.
- Exploratory Data Analysis with MATLAB by Wendy Martinez and Angel Martinez.
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
- Data Analysis and Graphics Using R: An Example-Based Approach by John Maindonald and W. John Braun
- Detection, Estimation, and Modulation Theory Part 1 Harry L. Van Trees.
- Detection, Estimation and Classification: An Introduction to Pattern Recognition and Related Topics by C. W. Therrien
- Computer Approaches to Pattern Recognition by William S. Meisel.
- Optimal Decision Theory by William DeGroot.
- Optimal Control and Estimation by Robert F. Stengel.
- Kalman Filtering Theory and Practice using MATLAB by Mohinder Grewal and Angus Andrews.
- Applied Optimal Estimation edited by Arthur Gelb.
- Resampling Methods: A Practical Guide to Data Analysis by Phillip Good
- Pattern Recognition: A Statistical Approach by Pierre A. Devijver and Josef Kittler.
- Introducing Monte Carlo Methods with R Christian P. Robert and George Casella.
- A TextBook of Convergence by William Leonard Ferrar
- Differential Equations by William E. Boyce and Richard DiPrima.
- Calculus by William E. Boyce and Richard DiPrima.
- The Calculus - A Genetic Approach by Otto Toeplitz.
- Introduction to Calculus and Analysis by Richard Courant and Fritz John.
- A Course of Modern Analysis by E. T. Whittaker and G. N. Watson.
- Ordinary Differential Equations by Edward L. Ince.
- Div, Grad, Curl, and All That: An Informal Text on Vector Calculus by H. M. Schey
- Asymptotics and Special Functions by Frank W. J. Olver.
- Nonlinear dynamics and chaos by Steven H. Strogatz.
- Linear Difference Equations by Kenneth S. Miller.
- Difference Equations: An Introduction with Applications by Walter G. Kelley and Allan C. Peterson.
- Introduction to Linear Algebra by Gilbert Strang.
- Fundamentals of MATRIX COMPUTATIONS by David S. Watkins.
- A Multigrid Tutorial by D. Briggs
- Matrix Computations by G. Golub and C. van Loan.
- APPLIED LINEAR ALGEBRA: The Decoupling Principle by Lorenzo Sadun.
- An Introduction to Multigrid Methods by Pieter Wesseling
- Applications of Linear Algebra by Chris Rorres and Howard Anton
- Matrices and Linear Transformations by Anthony J. Pettofrezzo.
- Elementary Numerical Analysis by Samuel Daniel Conte and Carl de Boor
- A First Look at Numerical Functional Analysis by W. W. Sawyer
- Numerical Methods that Work by Forman S. Acton
- Numerical Computing With Matlab by Cleve Molder.
- Finite-difference Equations and Simulations by Francis Begnaud Hildebrand.
- Afternotes on Numerical Analysis by G. W. Stewart.
- Numerical Methods Using Matlab by G. Lindfield and J. Penny
- Numerical Methods in Engineering with Python by Jaan Kiusalaas
- Numerical Analysis by David Kincaid and Ward Cheney
- Introduction to Finite Mathematics by J. Kemeny, J. Snell, and G. Thompson.
- An Introduction to Genetic Algorithms by Melanie Mitchell.
- Practical Genetic Algorithms by Randy L. Haupt and Sue Ellen Haupt.
- Numerical Optimization by J. Nocedal and S. Wright.
- Optimum Seeking Methods by J. Wilde
- Introduction to Linear Optimization by Dimitris Bertsimas and John N. Tsitsiklis.
Analytic Solution Techniques for Parital Differential Equations
- Second Course in Ordinary Differential Equations for Scientists and Engineers by Mayer Humi and William Miller
- An Introduction to the Method of Characteristics by Michael B. Abbott
- Partial Differential Equations by Lawrence C. Evans.
- Partial Differential Equations; Analytical Solution Techniques by J. Kevorkian.
- Linear Integral Equations by Rainer Kress.
- A Primer On Integral Equations of the First Kind by G. Milton Wing.
- Nonlinear Partial Differential Equations for Scientists and Engineers by Lokenath Debnath.
Numerical Solution Techniques for Differential Equations
- Numerical Computation of Internal and External Flows: Volume 1 & 2 by C. Hirsch
- The Finite Element Method: Basic Concepts and Applications by D. Pepper and J. Heinrich.
- Finite Volume Methods for Hyperbolic Problems by Randall J. LeVeque.
- Numerical Methods for Conservation Laws by Randall J. LeVeque.
- Linear and Nonlinear Waves by Gerald Beresford Whitham.
- Supersonic Flow and Shock Waves by Richard Courant.
- Thermo-dynamics by Enrico Fermi.
- Introduction to Thermal Physics by Daniel V. Schroeder
- Statistical Physics by Gregory H. Wannier.
- Fundamentals of Statistical and Thermal Physics by Frederick Reif.
- Stress Waves in Solids by H. Kolsky.
- Mathematical Models of Fluiddynamics: An Introduction by Rainer Ansorge.
- Elementary Fluid Dynamics by D. J. Acheson.
- Introduction to Wave Propagation in Nonlinear Fluids and Solids by Douglas S. Drumheller.
- A First Course in Probability by Sheldon Ross.
- Introduction to PROBABILITY MODELS: Seventh Edition by Sheldon M. Ross.
- Applied Probability Models with Optimization Applications by Sheldon M. Ross.
- An Elementary Introduction to Mathematical Finance by Sheldon M. Ross.
- Introduction to Stochastic Models by Roe Goodman.
- Basic Concepts of Probability and Statistics by J. L. Hodges and E. L. Lehmann.
- The Mathematics of Financial Derivatives by P. Wilmott
- Paul Wilmott on Quantitative Finance by P. Wilmott
- Frequently Asked Questions in Quantitative Finance by P. Wilmott
- Computational Finance Using C and C# by Georege Levy.
- Statistics and Data Analysis for Financial Engineering by David Ruppert
- Statistics and Finance: An Introduction by David Ruppert
- Empirical Market Microstructure by Joel Hasbrouck.
- Investement Science by David Luenberger.
Computer Science/Programming Languages:
- grokking algorithms by Aditay Bhargava.
- SQL Practice Problems by Sylvia Moestl Vasilik.
- Introduction to ALGORITHMS by T. Cormen, C. Leiserson, and R. Rivest.
- Parallel Programming with MPI by Peter Pacheco
- Introduction to Parallel Computing by Vipin Kumar, Ananth Grama, Anshul Gupta, & George Karypis.
- Foundations of Multithreaded, Parallel, and Distributed Programming by Gregory R. Andrews
- Unix Utilities by Ramkrishna S. Tare.
- Learning Python by Mark Lutz & David Ascher.
- Core Python Applications by Wesley Chun.
- Learning Perl by Randal L. Schwartz, Tom Phoenix, and Brian Foy.
- THE ELEMENTS OF PROGRAMMING STYLE by Brian W. Kernighan and P. J. Plauger.
- Mathematics Models of the Real World by Peter Lancaster
- Street-Fighting Mathematics by Sanjoy Mahajan
- Codes, Ciphers, and Secret Writing by Martin Gardner
- The Contest Problem Books: Annual High School Mathematics Contests
- Fantastic Book of Math Puzzles by Margaret Edmiston
- Math Puzzles and Games by Michael Holt
- Puzzles to Puzzle You by Shakuntala Devi
- How Would You Move Mt. Fuji? and Are You Smart Enough to Work at Google by William Poundstone
- Signals and Systems by Alan Oppenheim and Alan Willsky
- Elements of Information Theory by Thomas M. Cover and Joy A. Thomas
- Computer Simulation Using Particles by R.W. Hockney and J.W. Eastwood
- Topics in Applied Physics: Volume 25 Laser Beam Propagation in the Atmosphere; Edited by: John W. Strohbehn.
- Understanding LASER technology: Second Edition by C. Breck Hitz.
- Mechanics of Motor Proteins and the Cytoskeleton by Jonathon Howard.
- Science & Music by Sir James Jeans.
- Innumeracy: Mathematical Illiteracy and Its Consequences by John Allen Paulos.
- Radar Principles for the Non-Specialist by J. C. Toomay.
- Approximation Theory: From Taylor Polynomials to Wavelets by Ole Christensen and Khadija Laghrida Christensen.
- FOURIER ANALYSIS AND GENERALISED FUNCTIONS by M. J. Lighthill F.R.S.
- Modeling Differential Equations in Biology by Clifford Henry Taubes.
- Introduction to Graph Theory by Richard J. Trudeau.
- Generatingfunctionology: Second Edition by Herbert S. Wilf.
- Slicing Pizzas, Racing Turtles, and Further Adventures in Applied Mathematics by Robert B. Banks.