**Mathematical Methods In Data Science**

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### Data Science and Machine Learning

- Author : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
- Publisher : CRC Press
- Release Date : 2019-11-20
- Total pages : 538
- ISBN : 9781000730777
- File Size : 22,5 Mb
- Total Download : 292
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"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!" -Nicholas Hoell, University of Toronto "This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. -Adam Loy, Carleton College The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: Focuses on mathematical understanding. Presentation is self-contained, accessible, and comprehensive. Extensive list of exercises and worked-out examples. Many concrete algorithms with Python code. Full color throughout. Further Resources can be found on the authors website: https://github.com/DSML-book/Lectures

### Mathematical Methods in Data Science

- Author : Jingli Ren,Haiyan Wang
- Publisher : Elsevier
- Release Date : 2023-01-06
- Total pages : 260
- ISBN : 9780443186806
- File Size : 13,7 Mb
- Total Download : 897
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Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science. Combines a broad spectrum of mathematics, including linear algebra, optimization, network analysis and ordinary and partial differential equations for data science Written by two researchers who are actively applying mathematical and statistical methods as well as ODE and PDE for data analysis and prediction Highly interdisciplinary, with content spanning mathematics, data science, social media analysis, network science, financial markets, and more Presents a wide spectrum of topics in a logical order, including probability, linear algebra, calculus and optimization, networks, ordinary differential and partial differential equations

### Mathematics for Machine Learning

- Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
- Publisher : Cambridge University Press
- Release Date : 2020-04-23
- Total pages : 391
- ISBN : 9781108470049
- File Size : 20,9 Mb
- Total Download : 106
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Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

### Mathematical Problems in Data Science

- Author : Li M. Chen,Zhixun Su,Bo Jiang
- Publisher : Springer
- Release Date : 2015-12-15
- Total pages : 213
- ISBN : 9783319251271
- File Size : 20,9 Mb
- Total Download : 949
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This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.

### Data Science For Dummies

- Author : Lillian Pierson
- Publisher : John Wiley & Sons
- Release Date : 2017-03-06
- Total pages : 384
- ISBN : 9781119327639
- File Size : 20,9 Mb
- Total Download : 161
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Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

### The Mathematics of Data

- Author : Michael W. Mahoney,John C. Duchi,Anna C. Gilbert
- Publisher : American Mathematical Soc.
- Release Date : 2018-11-15
- Total pages : 325
- ISBN : 9781470435752
- File Size : 54,7 Mb
- Total Download : 554
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### Mathematical Foundations for Data Analysis

- Author : Jeff M. Phillips
- Publisher : Springer Nature
- Release Date : 2021-03-29
- Total pages : 299
- ISBN : 9783030623418
- File Size : 30,5 Mb
- Total Download : 208
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This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.

### Foundations of Data Science

- Author : Avrim Blum,John Hopcroft,Ravindran Kannan
- Publisher : Cambridge University Press
- Release Date : 2020-01-23
- Total pages : 433
- ISBN : 9781108485067
- File Size : 19,5 Mb
- Total Download : 188
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Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

### Data-Driven Science and Engineering

- Author : Steven L. Brunton,J. Nathan Kutz
- Publisher : Cambridge University Press
- Release Date : 2022-05-05
- Total pages : 615
- ISBN : 9781009098489
- File Size : 20,5 Mb
- Total Download : 565
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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

### Data Science and Machine Learning

- Author : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
- Publisher : CRC Press
- Release Date : 2019
- Total pages : 510
- ISBN : 1138492531
- File Size : 46,7 Mb
- Total Download : 197
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"The purpose of this book is to provide an accessible, yet comprehensive, account of data science and machine learning. It is intended for anyone interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science"--

### Mathematical Methods in the Earth and Environmental Sciences

- Author : Adrian Burd
- Publisher : Cambridge University Press
- Release Date : 2019-04-18
- Total pages : 599
- ISBN : 9781107117488
- File Size : 9,5 Mb
- Total Download : 631
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An accessible introduction to the mathematical methods essential for understanding processes in the Earth and environmental sciences.

### Mathematical Methods for Knowledge Discovery and Data Mining

- Author : Felici, Giovanni,Vercellis, Carlo
- Publisher : IGI Global
- Release Date : 2007-10-31
- Total pages : 394
- ISBN : 9781599045306
- File Size : 14,5 Mb
- Total Download : 887
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"This book focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance, manufacturing, marketing, performance measurement, and telecommunications"--Provided by publisher.

### Mathematical Pictures at a Data Science Exhibition

- Author : Simon Foucart
- Publisher : Cambridge University Press
- Release Date : 2022-04-28
- Total pages : 339
- ISBN : 9781316518885
- File Size : 28,8 Mb
- Total Download : 332
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A diverse selection of data science topics explored through a mathematical lens.

### Data Science for Mathematicians

- Author : Nathan Carter
- Publisher : CRC Press
- Release Date : 2020-09-15
- Total pages : 528
- ISBN : 9780429675683
- File Size : 14,9 Mb
- Total Download : 663
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Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

### Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications

- Author : Monica Bianchini,Maria Lucia Sampoli
- Publisher : Mdpi AG
- Release Date : 2022-01-12
- Total pages : 102
- ISBN : 3036528407
- File Size : 11,5 Mb
- Total Download : 251
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Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or process, but it is based on fundamental laws of physics or engineering that lead to systems of equations able to represent all the variables that characterize the process. Conversely, Machine Learning methods require a large amount of data to find solutions, remaining detached from the problem that generated them and trying to infer the behavior of the object, material or process to be examined from observed samples. Mathematics allows us to formulate complex models with effectiveness and creativity, describing nature and physics. Together with the potential of Artificial Intelligence and data collection techniques, a new way of dealing with practical problems is possible. The insertion of the equations deriving from the physical world in the data-driven models can in fact greatly enrich the information content of the sampled data, allowing to simulate very complex phenomena, with drastically reduced calculation times. Combined approaches will constitute a breakthrough in cutting-edge applications, providing precise and reliable tools for the prediction of phenomena in biological macro/microsystems, for biotechnological applications and for medical diagnostics, particularly in the field of precision medicine.

### Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications

- Author : T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
- Publisher : John Wiley & Sons
- Release Date : 2021-09-08
- Total pages : 370
- ISBN : 9781119785378
- File Size : 33,6 Mb
- Total Download : 302
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SIMULATIONS AND ANALYSIS of Mathematical Methods Written and edited by a group of international experts in the field, this exciting new volume covers the state of the art of real-time applications of computer science using mathematics. This breakthrough edited volume highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers. The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientist’s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

### Mathematical Foundations of Data Science Using R

- Author : Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer
- Publisher : Walter de Gruyter GmbH & Co KG
- Release Date : 2022-10-24
- Total pages : 424
- ISBN : 9783110796063
- File Size : 25,9 Mb
- Total Download : 991
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The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.

### Mathematical Methods in Science

- Author : George Pólya
- Publisher : Cambridge University Press
- Release Date : 1977
- Total pages : 252
- ISBN : 0883856263
- File Size : 21,6 Mb
- Total Download : 510
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'Mathematics, taught and learned appropriately, improves the mind and implants good habits of thought.' This tenet underlies all of Professor Pólya's works on teaching and problem-solving. This book captures some of Pólya's excitement and vision. In it he provides enlightenment for all those who have ever wondered how the laws of nature were worked out mathematically. The distinctive feature of the present book is the stress on the history of certain elementary chapters of science; these can be a source of enjoyment and deeper understanding of mathematics even for beginners who have little, or perhaps no, knowledge of physics.

### High-Dimensional Probability

- Author : Roman Vershynin
- Publisher : Cambridge University Press
- Release Date : 2018-09-27
- Total pages : 299
- ISBN : 9781108415194
- File Size : 14,9 Mb
- Total Download : 348
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An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

### Advanced Statistical Methods in Data Science

- Author : Ding-Geng Chen,Jiahua Chen,Xuewen Lu,Grace Y. Yi,Hao Yu
- Publisher : Springer
- Release Date : 2016-11-30
- Total pages : 222
- ISBN : 9789811025945
- File Size : 29,6 Mb
- Total Download : 812
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This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.

### Mathematical Methods for Signal and Image Analysis and Representation

- Author : Luc Florack,Remco Duits,Geurt Jongbloed,Marie-Colette van Lieshout,Laurie Davies
- Publisher : Springer Science & Business Media
- Release Date : 2012-01-12
- Total pages : 320
- ISBN : 9781447123538
- File Size : 47,7 Mb
- Total Download : 501
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Mathematical Methods for Signal and Image Analysis and Representation presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold (typically, but not necessarily, a subset of spacetime). The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies. Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built, making it an ideal companion for those seeking a rigorous methodological basis for specific algorithms as well as for those interested in the fundamental methodology per se. Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception.