An Excursion Into Statistical Learning
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An Excursion Into Statistical Learning
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-05-07
An Excursion Into Statistical Learning written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Science categories.
Embark on a journey into the realm of statistical learning, where data transforms into knowledge and insights emerge from uncertainty. "An Excursion into Statistical Learning" is a comprehensive guide, meticulously crafted to unveil the power of statistical learning and empower you to harness its potential. Within these pages, you'll delve into the fundamental concepts of probability, the bedrock of statistical analysis. Explore probability axioms, conditional probability, Bayes' theorem, random variables, and probability distributions, gaining a solid foundation for understanding statistical inference. Unravel the intricacies of statistical inference, mastering point estimation, confidence intervals, hypothesis testing, and regression analysis. Discover how statistical models illuminate data, enabling you to draw informed conclusions and make data-driven decisions. Venture into the captivating world of machine learning, where algorithms learn from data, uncovering patterns and making predictions. Delve into supervised learning methods, such as decision trees, support vector machines, and random forests, unlocking their ability to make accurate predictions based on labeled data. Explore unsupervised learning methods, such as k-means clustering, hierarchical clustering, and principal component analysis, unveiling hidden structures and patterns within uncharted data. Recognize the significance of data preparation and exploration, the crucial steps that lay the foundation for successful statistical learning. Immerse yourself in data cleaning and preprocessing techniques, transforming raw data into a suitable format for analysis. Utilize exploratory data analysis methods, such as visualization and summary statistics, to uncover hidden insights and guide the selection of appropriate statistical models. Equip yourself with advanced statistical modeling techniques, venturing beyond the basics. Explore generalized linear models, time series analysis, survival analysis, and mixed-effects models, delving into their applications across diverse domains. Discover Bayesian statistics and graphical models, frameworks that incorporate prior knowledge and model complex dependencies. As you navigate the world of statistical learning, embrace the ethical and responsible use of these powerful techniques. Examine algorithmic bias, data privacy, and the paramount importance of transparency and interpretability in statistical models. Promote diversity and inclusion in the field of statistical learning, advocating for a responsible and ethical approach to data analysis. If you like this book, write a review on google books!
Excursions In Harmonic Analysis Volume 5
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Author : Radu Balan
language : en
Publisher: Birkhäuser
Release Date : 2017-06-20
Excursions In Harmonic Analysis Volume 5 written by Radu Balan and has been published by Birkhäuser this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-20 with Mathematics categories.
This volume consists of contributions spanning a wide spectrum of harmonic analysis and its applications written by speakers at the February Fourier Talks from 2002 – 2016. Containing cutting-edge results by an impressive array of mathematicians, engineers, and scientists in academia, industry and government, it will be an excellent reference for graduate students, researchers, and professionals in pure and applied mathematics, physics, and engineering. Topics covered include: Theoretical harmonic analysis Image and signal processing Quantization Algorithms and representations The February Fourier Talks are held annually at the Norbert Wiener Center for Harmonic Analysis and Applications. Located at the University of Maryland, College Park, the Norbert Wiener Center provides a state-of- the-art research venue for the broad emerging area of mathematical engineering.
Supervised Learning With Quantum Computers
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Author : Maria Schuld
language : en
Publisher: Springer
Release Date : 2018-08-30
Supervised Learning With Quantum Computers written by Maria Schuld and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Science categories.
Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
A Guided Tour Of Artificial Intelligence Research
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Author : Pierre Marquis
language : en
Publisher: Springer Nature
Release Date : 2020-05-08
A Guided Tour Of Artificial Intelligence Research written by Pierre Marquis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-08 with Technology & Engineering categories.
The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.
Recent Advances And Trends In Nonparametric Statistics
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Author : M.G. Akritas
language : en
Publisher: Elsevier
Release Date : 2003-10-31
Recent Advances And Trends In Nonparametric Statistics written by M.G. Akritas and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-10-31 with Computers categories.
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium. Key features: . algorithic approaches . wavelets and nonlinear smoothers . graphical methods and data mining . biostatistics and bioinformatics . bagging and boosting . support vector machines . resampling methods
Excursions In Harmonic Analysis Volume 4
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Author : Radu Balan
language : en
Publisher: Birkhäuser
Release Date : 2015-10-20
Excursions In Harmonic Analysis Volume 4 written by Radu Balan and has been published by Birkhäuser this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-20 with Mathematics categories.
This volume consists of contributions spanning a wide spectrum of harmonic analysis and its applications written by speakers at the February Fourier Talks from 2002 – 2013. Containing cutting-edge results by an impressive array of mathematicians, engineers and scientists in academia, industry and government, it will be an excellent reference for graduate students, researchers and professionals in pure and applied mathematics, physics and engineering. Topics covered include: Special Topics in Harmonic Analysis Applications and Algorithms in the Physical Sciences Gabor Theory RADAR and Communications: Design, Theory, and Applications The February Fourier Talks are held annually at the Norbert Wiener Center for Harmonic Analysis and Applications. Located at the University of Maryland, College Park, the Norbert Wiener Center provides a state-of- the-art research venue for the broad emerging area of mathematical engineering.
How Children Learn To Write Words
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Author : Rebecca Treiman
language : en
Publisher:
Release Date : 2014
How Children Learn To Write Words written by Rebecca Treiman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Language Arts & Disciplines categories.
Writing allows people to convey information to others who are remote in time and space, vastly increasing the range over which people can cooperate and the amount they can learn. Mastering the writing system of one's language is crucial for success in a modern society. This book examines how children learn to write words. It provides a theoretical framework that integrates findings from a wide range of age groups - from children who are producing their first scribbles to experienced spellers who are writing complex words. To set the stage for these discussions, early chapters of the book consider the nature of writing systems and the nature of learning itself. The following chapters review various aspects of orthographic development, including the learning of symbol shapes and punctuation. Each chapter reviews research with learners of a variety of languages and writing systems, revealing underlying similarities. Discussions of how orthography is and should be taught are incorporated into each chapter, making the book of interest to educators as well as to psychologists, cognitive scientists, and linguists. This book is unique in the range of topics and languages that it covers and the degree to which it integrates linguistic insights about the nature of writing systems with discussions of how people learn to use these systems. It is written in a scholarly yet accessible manner, making it suited for a wide audience.
Excursions In Harmonic Analysis Volume 6
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Author : Matthew Hirn
language : en
Publisher: Springer Nature
Release Date : 2021-09-01
Excursions In Harmonic Analysis Volume 6 written by Matthew Hirn and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Mathematics categories.
John J. Benedetto has had a profound influence not only on the direction of harmonic analysis and its applications, but also on the entire community of people involved in the field. The chapters in this volume – compiled on the occasion of his 80th birthday – are written by leading researchers in the field and pay tribute to John’s many significant and lasting achievements. Covering a wide range of topics in harmonic analysis and related areas, these chapters are organized into four main parts: harmonic analysis, wavelets and frames, sampling and signal processing, and compressed sensing and optimization. An introductory chapter also provides a brief overview of John’s life and mathematical career. This volume will be an excellent reference for graduate students, researchers, and professionals in pure and applied mathematics, engineering, and physics.
Tree Based Methods For Statistical Learning In R
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Author : Brandon M. Greenwell
language : en
Publisher: CRC Press
Release Date : 2022-06-23
Tree Based Methods For Statistical Learning In R written by Brandon M. Greenwell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Business & Economics categories.
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e.g., Python, Spark, and Julia), and example usage on real data sets. While the book mostly uses R, it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.
A Tour Of Data Science
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Author : Nailong Zhang
language : en
Publisher: CRC Press
Release Date : 2020-11-11
A Tour Of Data Science written by Nailong Zhang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-11 with Computers categories.
A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.