Inference And Learning From Data Volume 1

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Inference and Learning from Data: Volume 1

Ali H. Sayed
Inference and Learning from Data Volume 1 Book PDF

Author : Ali H. Sayed
Publisher : Cambridge University Press
Page : 1106 pages
File Size : 41,9 Mb
Release : 2022-12-22
Category : Technology & Engineering
ISBN : 9781009218139

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Inference and Learning from Data: Volume 1 by Ali H. Sayed Pdf

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

Inference and Learning from Data: Volume 3

Ali H. Sayed
Inference and Learning from Data Volume 3 Book PDF

Author : Ali H. Sayed
Publisher : Cambridge University Press
Page : 1082 pages
File Size : 52,5 Mb
Release : 2022-12-22
Category : Technology & Engineering
ISBN : 9781009218306

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Inference and Learning from Data: Volume 3 by Ali H. Sayed Pdf

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.

Information Theory, Inference and Learning Algorithms

David J. C. MacKay
Information Theory Inference and Learning Algorithms Book PDF

Author : David J. C. MacKay
Publisher : Cambridge University Press
Page : 694 pages
File Size : 50,7 Mb
Release : 2003-09-25
Category : Computers
ISBN : 0521642981

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Information Theory, Inference and Learning Algorithms by David J. C. MacKay Pdf

Table of contents

Inference and Learning from Data

Ali H. Sayed
Inference and Learning from Data Book PDF

Author : Ali H. Sayed
Publisher : Cambridge University Press
Page : 1165 pages
File Size : 41,7 Mb
Release : 2022-11-30
Category : Computers
ISBN : 9781009218269

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Inference and Learning from Data by Ali H. Sayed Pdf

Discover techniques for inferring unknown variables and quantities with the second volume of this extraordinary three-volume set.

Inference and Learning from Data: Volume 2

Ali H. Sayed
Inference and Learning from Data Volume 2 Book PDF

Author : Ali H. Sayed
Publisher : Cambridge University Press
Page : 1166 pages
File Size : 42,5 Mb
Release : 2022-12-22
Category : Technology & Engineering
ISBN : 9781009218252

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Inference and Learning from Data: Volume 2 by Ali H. Sayed Pdf

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

Deep Learning

Ian Goodfellow,Yoshua Bengio,Aaron Courville
Deep Learning Book PDF

Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Page : 801 pages
File Size : 47,5 Mb
Release : 2016-11-10
Category : Computers
ISBN : 9780262337373

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Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

The Elements of Statistical Learning

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

Author : Trevor Hastie,Robert Tibshirani,Jerome Friedman
Publisher : Springer Science & Business Media
Page : 545 pages
File Size : 54,5 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9780387216065

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

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Chester Ismay,Albert Y. Kim
Statistical Inference via Data Science A ModernDive into R and the Tidyverse Book PDF

Author : Chester Ismay,Albert Y. Kim
Publisher : CRC Press
Page : 461 pages
File Size : 42,7 Mb
Release : 2019-12-23
Category : Mathematics
ISBN : 9781000763461

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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse by Chester Ismay,Albert Y. Kim Pdf

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.

Scientific Inference

Simon Vaughan
Scientific Inference Book PDF

Author : Simon Vaughan
Publisher : Cambridge University Press
Page : 239 pages
File Size : 49,5 Mb
Release : 2013-09-19
Category : Mathematics
ISBN : 9781107024823

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Scientific Inference by Simon Vaughan Pdf

Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice.

Learning from Data

Vladimir Cherkassky,Filip M. Mulier
Learning from Data Book PDF

Author : Vladimir Cherkassky,Filip M. Mulier
Publisher : John Wiley & Sons
Page : 560 pages
File Size : 45,5 Mb
Release : 2007-09-10
Category : Computers
ISBN : 0470140518

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Learning from Data by Vladimir Cherkassky,Filip M. Mulier Pdf

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.

Doing Data Science

Cathy O'Neil,Rachel Schutt
Doing Data Science Book PDF

Author : Cathy O'Neil,Rachel Schutt
Publisher : "O'Reilly Media, Inc."
Page : 408 pages
File Size : 48,9 Mb
Release : 2013-10-09
Category : Computers
ISBN : 9781449363895

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Doing Data Science by Cathy O'Neil,Rachel Schutt Pdf

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Mathematics for Machine Learning

Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Mathematics for Machine Learning Book PDF

Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Page : 391 pages
File Size : 55,8 Mb
Release : 2020-04-23
Category : Computers
ISBN : 9781108470049

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Mathematics for Machine Learning by Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong Pdf

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Probabilistic Machine Learning

Kevin P. Murphy
Probabilistic Machine Learning Book PDF

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 858 pages
File Size : 50,7 Mb
Release : 2022-03-01
Category : Computers
ISBN : 9780262369305

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Probabilistic Machine Learning by Kevin P. Murphy Pdf

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Machine Learning

Kevin P. Murphy
Machine Learning Book PDF

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 1102 pages
File Size : 47,6 Mb
Release : 2012-08-24
Category : Computers
ISBN : 9780262018029

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Machine Learning by Kevin P. Murphy Pdf

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Modern Statistics with R

Måns Thulin
Modern Statistics with R Book PDF

Author : Måns Thulin
Publisher : BoD - Books on Demand
Page : 598 pages
File Size : 43,9 Mb
Release : 2021-07-28
Category : Mathematics
ISBN : 9789152701515

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Modern Statistics with R by Måns Thulin Pdf

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you: - Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. - Exploratory data analysis - using visualisation and multivariate techniques to explore datasets. - Statistical inference - modern methods for testing hypotheses and computing confidence intervals. - Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. - Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. - Ethics in statistics - ethical issues and good statistical practice. - R programming - writing code that is fast, readable, and free from bugs. Starting from the very basics, Modern Statistics with R helps you learn R by working with R. Topics covered range from plotting data and writing simple R code to using cross-validation for evaluating complex predictive models and using simulation for sample size determination. The book includes more than 200 exercises with fully worked solutions. Some familiarity with basic statistical concepts, such as linear regression, is assumed. No previous programming experience is needed.