The 65 books list:

• The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman

• Introductory Time Series with R Paul S.P. Cowpertwait, Andrew V. Metcalfe

• A Beginner’s Guide to R Alain Zuur, Elena N. Ieno, Erik Meesters

• Introduction to Evolutionary Computing A.E. Eiben, J.E. Smith

• Data Analysis Siegmund Brandt

• Linear and Nonlinear Programming David G. Luenberger, Yinyu Ye

• Introduction to Partial Differential Equations David Borthwick

• Fundamentals of Robotic Mechanical Systems Jorge Angeles

• Data Structures and Algorithms with Python Kent D. Lee, Steve Hubbard

• Introduction to Partial Differential Equations Peter J. Olver

• Methods of Mathematical Modelling Thomas Witelski, Mark Bowen

• LaTeX in 24 Hours Dilip Datta

• Introduction to Statistics and Data Analysis Christian Heumann, Michael Schomaker, Shalabh

• Principles of Data Mining Max Bramer

• Computer Vision Richard Szeliski

• Data Mining Charu C. Aggarwal

• Computational Geometry Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars

• Robotics, Vision and Control Peter Corke

• Statistical Analysis and Data Display Richard M. Heiberger, Burt Holland

• Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson

• Stochastic Processes and Calculus Uwe Hassler

• Statistical Analysis of Clinical Data on a Pocket Calculator Ton J. Cleophas, Aeilko H. Zwinderman

• Clinical Data Analysis on a Pocket Calculator Ton J. Cleophas, Aeilko H. Zwinderman

• The Data Science Design Manual Steven S. Skiena

• An Introduction to Machine Learning Miroslav Kubat

• Guide to Discrete Mathematics Gerard O’Regan

• Introduction to Time Series and Forecasting Peter J. Brockwell, Richard A. Davis

• Multivariate Calculus and Geometry Seán Dineen

• Statistics and Analysis of Scientific Data Massimiliano Bonamente

• Modelling Computing Systems Faron Moller, Georg Struth

• Search Methodologies Edmund K. Burke, Graham Kendall

• Linear Algebra Done Right Sheldon Axler

• Linear Algebra Jörg Liesen, Volker Mehrmann

• Algebra Serge Lang

• Understanding Analysis Stephen Abbott

• Linear Programming Robert J Vanderbei

• Understanding Statistics Using R Randall Schumacker, Sara Tomek

• An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

• Statistical Learning from a Regression Perspective Richard A. Berk

• Applied Partial Differential Equations J. David Logan

• Robotics Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo

• Regression Modeling Strategies Frank E. Harrell , Jr.

• A Modern Introduction to Probability and Statistics F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester

• The Python Workbook Ben Stephenson

• Machine Learning in Medicine — a Complete Overview Ton J. Cleophas, Aeilko H. Zwinderman

• Object-Oriented Analysis, Design and Implementation Brahma Dathan, Sarnath Ramnath

• Introduction to Data Science Laura Igual, Santi Seguí

• Applied Predictive Modeling Max Kuhn, Kjell Johnson

• Python For ArcGIS Laura Tateosian

• Concise Guide to Databases Peter Lake, Paul Crowther

• Digital Image Processing Wilhelm Burger, Mark J. Burge

• Bayesian Essentials with R Jean-Michel Marin, Christian P. Robert

• Robotics, Vision and Control Peter Corke

• Foundations of Programming Languages Kent D. Lee

• Introduction to Artificial Intelligence Wolfgang Ertel

• Introduction to Deep Learning Sandro Skansi

• Linear Algebra and Analytic Geometry for Physical Sciences Giovanni Landi, Alessandro Zampini

• Applied Linear Algebra Peter J. Olver, Chehrzad Shakiban

• Neural Networks and Deep Learning Charu C. Aggarwal

• Data Science and Predictive Analytics Ivo D. Dinov

• Analysis for Computer Scientists Michael Oberguggenberger, Alexander Ostermann

• Excel Data Analysis Hector Guerrero

• A Beginners Guide to Python 3 Programming John Hunt

• Advanced Guide to Python 3 Programming John Hunt