# Springer just released 65 books related to Machine Learning **URL:** \ **Category:** News & Articles\ **Tags:** ebook, programming, ai\ **Created:** [April 27, 2020, 6:27pm UTC](https://onehack.st/t/springer-just-released-65-books-related-to-machine-learning/82823 "2020-04-27T18:27:02Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![Experimental](https://onehack.st/user_avatar/onehack.st/experimental/32/27753_2.png) [@Experimental](https://onehack.st/u/Experimental)\ **Post date:** [April 27, 2020, 6:27pm UTC](https://onehack.st/t/springer-just-released-65-books-related-to-machine-learning/82823/1 "2020-04-27T18:27:03Z")
# The 65 books list: **The Elements of Statistical Learning** Trevor Hastie, Robert Tibshirani, Jerome Friedman > **[The Elements of Statistical Learning](https://link.springer.com/book/10.1007/978-0-387-84858-7)** > > This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing. **Introductory Time Series with R** Paul S.P. Cowpertwait, Andrew V. Metcalfe > **[Introductory Time Series with R](https://link.springer.com/book/10.1007/978-0-387-88698-5)** > > This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it... **A Beginner’s Guide to R** Alain Zuur, Elena N. Ieno, Erik Meesters > **[A Beginner's Guide to R](https://link.springer.com/book/10.1007/978-0-387-93837-0)** > > Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum.... **Introduction to Evolutionary Computing** A.E. Eiben, J.E. Smith > **[Introduction to Evolutionary Computing](https://link.springer.com/book/10.1007/978-3-662-44874-8)** > > The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on... **Data Analysis** Siegmund Brandt > **[Data Analysis](https://link.springer.com/book/10.1007/978-3-319-03762-2)** > > The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The... **Linear and Nonlinear Programming** David G. Luenberger, Yinyu Ye > **[Linear and Nonlinear Programming](https://link.springer.com/book/10.1007/978-3-319-18842-3)** > > The 5th edition covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. **Introduction to Partial Differential Equations** David Borthwick > **[Introduction to Partial Differential Equations](https://link.springer.com/book/10.1007/978-3-319-48936-0)** > > This modern take on partial differential equations does not require knowledge beyond vector calculus and linear algebra. The author focuses on the most important classical partial differential equations, including conservation equations and their... **Fundamentals of Robotic Mechanical Systems** Jorge Angeles > **[Fundamentals of Robotic Mechanical Systems](https://link.springer.com/book/10.1007/978-3-319-01851-5)** > > The 4th edition includes updated and additional examples and exercises on the core fundamental concepts of mechanics, robots, and kinematics of serial robots. New images of CAD models and physical robots help to motivate concepts being introduced.... **Data Structures and Algorithms with Python** Kent D. Lee, Steve Hubbard > **[Data Structures and Algorithms with Python](https://link.springer.com/book/10.1007/978-3-319-13072-9)** > > This textbook explains data structures and algorithms concepts and techniques needed to write programs that can handle large amounts of data. **Introduction to Partial Differential Equations** Peter J. Olver > **[Introduction to Partial Differential Equations](https://link.springer.com/book/10.1007/978-3-319-02099-0)** > > This textbook is designed for a one year course covering the fundamentals of partial differential equations, geared towards advanced undergraduates and beginning graduate students in mathematics, science, engineering, and elsewhere. The exposition... **Methods of Mathematical Modelling** Thomas Witelski, Mark Bowen > **[Methods of Mathematical Modelling](https://link.springer.com/book/10.1007/978-3-319-23042-9)** > > This book presents mathematical modelling and the integrated process of formulating sets of equations to describe real-world problems. It describes methods for obtaining solutions of challenging differential equations stemming from problems in areas... **LaTeX in 24 Hours** Dilip Datta > **[LaTeX in 24 Hours](https://link.springer.com/book/10.1007/978-3-319-47831-9)** > > This book presents direct and concise explanations and examples to many LaTeX syntax and structures, allowing students and researchers to quickly understand the basics that are required for writing and preparing book manuscripts, journal articles,... **Introduction to Statistics and Data Analysis** Christian Heumann, Michael Schomaker, Shalabh > **[Introduction to Statistics and Data Analysis](https://link.springer.com/book/10.1007/978-3-319-46162-5)** > > The undergraduate textbook Introduction to Statistics and Data Analysis features a wealth of examples and exercises with R code. Discover the new edition. **Principles of Data Mining** Max Bramer > **[Principles of Data Mining](https://link.springer.com/book/10.1007/978-1-4471-7307-6)** > > This textbook explains the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on... **Computer Vision** Richard Szeliski > **[Computer Vision](https://link.springer.com/book/10.1007/978-1-84882-935-0)** > > This undergraduate textbook-reference comprehensively examines computer vision techniques, analysis, and real-world applications in which they are used. **Data Mining** Charu C. Aggarwal > **[Data Mining](https://link.springer.com/book/10.1007/978-3-319-14142-8)** > > This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data... **Computational Geometry** Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars > **[Computational Geometry](https://link.springer.com/book/10.1007/978-3-540-77974-2)** > > Computational geometry emerged from the ?eld of algorithms design and analysis in the late 1970s. It has grown into a recognized discipline with its own journals, conferences, and a large community of active researchers. The success of the ?eld as a... **Robotics, Vision and Control** Peter Corke > **[Robotics, Vision and Control](https://link.springer.com/book/10.1007/978-3-319-54413-7)** > > This book explains how to choose the right algorithm to decompose and solve complex problems, with simple lines of code . **Statistical Analysis and Data Display** Richard M. Heiberger, Burt Holland > **[Statistical Analysis and Data Display](https://link.springer.com/book/10.1007/978-1-4939-2122-5)** > > This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The authors demonstrate how to analyze data—showing code, graphics, and accompanying tabular... **Statistics and Data Analysis for Financial Engineering** David Ruppert, David S. Matteson > **[Statistics and Data Analysis for Financial Engineering](https://link.springer.com/book/10.1007/978-1-4939-2614-5)** > > The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R... **Stochastic Processes and Calculus** Uwe Hassler > **[Stochastic Processes and Calculus](https://link.springer.com/book/10.1007/978-3-319-23428-1)** > > This textbook gives a comprehensive introduction to stochastic processes and calculus in the fields of finance and economics, more specifically mathematical finance and time series econometrics. Over the past decades stochastic calculus and processes... **Statistical Analysis of Clinical Data on a Pocket Calculator** Ton J. Cleophas, Aeilko H. Zwinderman > **[Clinical Data Analysis on a Pocket Calculator](https://link.springer.com/book/10.1007/978-94-007-1211-9)** > > In > medical and health care the scientific method is little used, and statistical > software programs are experienced as black box programs producing lots of > p-values, but little answers to scientific questions. The pocket calculator > analyses appears to... **Clinical Data Analysis on a Pocket Calculator** Ton J. Cleophas, Aeilko H. Zwinderman > **[Clinical Data Analysis on a Pocket Calculator](https://link.springer.com/book/10.1007/978-3-319-27104-0)** > > In > medical and health care the scientific method is little used, and statistical > software programs are experienced as black box programs producing lots of > p-values, but little answers to scientific questions. The pocket calculator > analyses appears to... **The Data Science Design Manual** Steven S. Skiena > **[The Data Science Design Manual](https://link.springer.com/book/10.1007/978-3-319-55444-0)** > > This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills... **An Introduction to Machine Learning** Miroslav Kubat > **[An Introduction to Machine Learning](https://link.springer.com/book/10.1007/978-3-319-63913-0)** > > The Third Edition of this textbook offers a comprehensive introduction to Machine Learning techniques and algorithms, in an easy-to-understand manner. **Guide to Discrete Mathematics** Gerard O’Regan > **[Guide to Discrete Mathematics](https://link.springer.com/book/10.1007/978-3-319-44561-8)** > > This textbook presents a broad and accessible introduction of discrete mathematics and presents a strong focus on applications to coding and graph theory **Introduction to Time Series and Forecasting** Peter J. Brockwell, Richard A. Davis > **[Introduction to Time Series and Forecasting](https://link.springer.com/book/10.1007/978-3-319-29854-2)** > > This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and... **Multivariate Calculus and Geometry** Seán Dineen > **[Multivariate Calculus and Geometry](https://link.springer.com/book/10.1007/978-1-4471-6419-7)** > > Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook not only follows this programme, but additionally provides a solid description of the... **Statistics and Analysis of Scientific Data** Massimiliano Bonamente > **[Statistics and Analysis of Scientific Data](https://link.springer.com/book/10.1007/978-1-4939-6572-4)** > > This is the third edition of a modern statistics textbook for early graduate students, including Monte-Carlo Markov chain and low-count statistics **Modelling Computing Systems** Faron Moller, Georg Struth > **[Modelling Computing Systems](https://link.springer.com/book/10.1007/978-1-84800-322-4)** > > This engaging text presents the fundamental mathematics and modelling techniques for computing systems in a novel and light-hearted way, which can be easily followed by students at the very beginning of their university education. Key concepts are... **Search Methodologies** Edmund K. Burke, Graham Kendall > **[Search Methodologies](https://link.springer.com/book/10.1007/978-1-4614-6940-7)** > > The book provides useful guidelines for implementing methods, frameworks described and offers valuable tutorials to students and researchers in the field. **Linear Algebra Done Right** Sheldon Axler > **[Linear Algebra Done Right](https://link.springer.com/book/10.1007/978-3-319-11080-6)** > > The eBook of this fourth edition text for a second course in linear algebra is Open Access and aimed at undergrad math majors and graduate students. **Linear Algebra** Jörg Liesen, Volker Mehrmann > **[Linear Algebra](https://link.springer.com/book/10.1007/978-3-319-24346-7)** > > This undergraduate textbook on linear algebra takes a computational approach via matrices and includes applications. **Algebra** Serge Lang > **[Algebra](https://link.springer.com/book/10.1007/978-1-4613-0041-0)** > > From April 1999 Notices of the AMS, announcing that the author was awarded the Leroy P. Steele Prize for Mathematical Exposition for his many mathematics books: "Lang's Algebra changed the way graduate algebra is taught, retaining classical topics... **Understanding Analysis** Stephen Abbott > **[Understanding Analysis](https://link.springer.com/book/10.1007/978-1-4939-2712-8)** > > This lively introductory text exposes the student to the rewards of a rigorous study of functions of a real variable. In each chapter, informal discussions of questions that give analysis its inherent fascination are followed by precise, but not... **Linear Programming** Robert J Vanderbei > **[Linear Programming](https://link.springer.com/book/10.1007/978-1-4614-7630-6)** > > The book introduces both the theory and the application of optimization in the parametric self-dual simplex method. The latest edition now includes: modern Machine Learning applications; a section explaining Gomory Cuts and an application of integer... **Understanding Statistics Using R** Randall Schumacker, Sara Tomek > **[Understanding Statistics Using R](https://link.springer.com/book/10.1007/978-1-4614-6227-9)** > > This book was written to provide resource materials for teachers to use in their introductory or intermediate statistics class. The  chapter content is ordered along the lines of many popular statistics books so it should be easy to supplement the... **An Introduction to Statistical Learning** Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani > **[An Introduction to Statistical Learning](https://link.springer.com/book/10.1007/978-1-4614-7138-7)** > > This book provides an accessible overview of the field of statistical learning, with applications in R programming. **Statistical Learning from a Regression Perspective** Richard A. Berk > **[Statistical Learning from a Regression Perspective](https://link.springer.com/book/10.1007/978-3-319-44048-4)** > > As in prior editions, this textbook on statistical learning employs the unifying theme that supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those... **Applied Partial Differential Equations** J. David Logan > **[Applied Partial Differential Equations](https://link.springer.com/book/10.1007/978-3-319-12493-3)** > > This textbook is for the standard, one-semester, junior-senior course that often goes by the title "Elementary Partial Differential Equations" or "Boundary Value Problems". The audience consists of students in mathematics, engineering, and the... **Robotics** Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo > **[Robotics](https://link.springer.com/book/10.1007/978-1-84628-642-1)** > > The classic text on robot manipulators now covers visual control, motion planning and mobile robots too!Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic... **Regression Modeling Strategies** Frank E. Harrell , Jr. > **[Regression Modeling Strategies](https://link.springer.com/book/10.1007/978-3-319-19425-7)** > > This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which... **A Modern Introduction to Probability and Statistics** F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester > **[A Modern Introduction to Probability and Statistics](https://link.springer.com/book/10.1007/1-84628-168-7)** > > Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples,... **The Python Workbook** Ben Stephenson > **[The Python Workbook](https://link.springer.com/book/10.1007/978-3-319-14240-1)** > > This text develops programming skills through hands-on practice, offering exercises that span a variety of academic disciplines and everyday situations. **Machine Learning in Medicine — a Complete Overview** Ton J. Cleophas, Aeilko H. Zwinderman > **[Machine Learning in Medicine – A Complete Overview](https://link.springer.com/book/10.1007/978-3-319-15195-3)** > > This book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, must-read, not only for physicians and students, but also for any one involved in the process and progress of health and... **Object-Oriented Analysis, Design and Implementation** Brahma Dathan, Sarnath Ramnath > **[Object-Oriented Analysis, Design and Implementation](https://link.springer.com/book/10.1007/978-3-319-24280-4)** > > This revised 3rd edition textbook, emphasizes the importance of modeling and highlights software reuse by applying principles of substitutability **Introduction to Data Science** Laura Igual, Santi Seguí > **[Introduction to Data Science](https://link.springer.com/book/10.1007/978-3-319-50017-1)** > > This textbook introduces the fundamentals of the important and highly interdisciplinary field of data science. **Applied Predictive Modeling** Max Kuhn, Kjell Johnson > **[Applied Predictive Modeling](https://link.springer.com/book/10.1007/978-1-4614-6849-3)** > > Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and... **Python For ArcGIS** Laura Tateosian > **[Python For ArcGIS](https://link.springer.com/book/10.1007/978-3-319-18398-5)** > > This book introduces Python scripting for geographic information science (GIS) workflow optimization using ArcGIS. It builds essential programming skills for automating GIS analysis. Over 200 sample Python scripts and 175 classroom-tested exercises... **Concise Guide to Databases** Peter Lake, Paul Crowther > **[Concise Guide to Databases](https://link.springer.com/book/10.1007/978-1-4471-5601-7)** > > This undergraduate textbook presents a comprehensive and easy-to-read introduction to databases and innovative database technologies. **Digital Image Processing** Wilhelm Burger, Mark J. Burge [http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6684-9](http://link.springer.com/openurl?genre=book&isbn=978-1-4471-6684-9) **Bayesian Essentials with R** Jean-Michel Marin, Christian P. Robert [http://link.springer.com/openurl?genre=book&isbn=978-1-4614-8687-9](http://link.springer.com/openurl?genre=book&isbn=978-1-4614-8687-9) **Robotics, Vision and Control** Peter Corke [http://link.springer.com/openurl?genre=book&isbn=978-3-642-20144-8](http://link.springer.com/openurl?genre=book&isbn=978-3-642-20144-8) **Foundations of Programming Languages** Kent D. Lee [http://link.springer.com/openurl?genre=book&isbn=978-3-319-70790-7](http://link.springer.com/openurl?genre=book&isbn=978-3-319-70790-7) **Introduction to Artificial Intelligence** Wolfgang Ertel [http://link.springer.com/openurl?genre=book&isbn=978-3-319-58487-4](http://link.springer.com/openurl?genre=book&isbn=978-3-319-58487-4) **Introduction to Deep Learning** Sandro Skansi [http://link.springer.com/openurl?genre=book&isbn=978-3-319-73004-2](http://link.springer.com/openurl?genre=book&isbn=978-3-319-73004-2) **Linear Algebra and Analytic Geometry for Physical Sciences** Giovanni Landi, Alessandro Zampini [http://link.springer.com/openurl?genre=book&isbn=978-3-319-78361-1](http://link.springer.com/openurl?genre=book&isbn=978-3-319-78361-1) **Applied Linear Algebra** Peter J. Olver, Chehrzad Shakiban [http://link.springer.com/openurl?genre=book&isbn=978-3-319-91041-3](http://link.springer.com/openurl?genre=book&isbn=978-3-319-91041-3) **Neural Networks and Deep Learning** Charu C. Aggarwal [http://link.springer.com/openurl?genre=book&isbn=978-3-319-94463-0](http://link.springer.com/openurl?genre=book&isbn=978-3-319-94463-0) **Data Science and Predictive Analytics** Ivo D. Dinov [http://link.springer.com/openurl?genre=book&isbn=978-3-319-72347-1](http://link.springer.com/openurl?genre=book&isbn=978-3-319-72347-1) **Analysis for Computer Scientists** Michael Oberguggenberger, Alexander Ostermann [http://link.springer.com/openurl?genre=book&isbn=978-3-319-91155-7](http://link.springer.com/openurl?genre=book&isbn=978-3-319-91155-7) **Excel Data Analysis** Hector Guerrero [http://link.springer.com/openurl?genre=book&isbn=978-3-030-01279-3](http://link.springer.com/openurl?genre=book&isbn=978-3-030-01279-3) **A Beginners Guide to Python 3 Programming** John Hunt [http://link.springer.com/openurl?genre=book&isbn=978-3-030-20290-3](http://link.springer.com/openurl?genre=book&isbn=978-3-030-20290-3) **Advanced Guide to Python 3 Programming** John Hunt [http://link.springer.com/openurl?genre=book&isbn=978-3-030-25943-3](http://link.springer.com/openurl?genre=book&isbn=978-3-030-25943-3) `Source : towardsdatascience` ---
**Author:** ![Pawan\_Kumar](https://onehack.st/user_avatar/onehack.st/pawan_kumar/32/24681_2.png) [@Pawan\_Kumar](https://onehack.st/u/Pawan_Kumar)\ **Post date:** [April 28, 2020, 3:41am UTC](https://onehack.st/t/springer-just-released-65-books-related-to-machine-learning/82823/2 "2020-04-28T03:41:57Z")
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