Articles by "Machine Learning"

Showing posts with label Machine Learning. Show all posts

Shuyu Sun, Tao Zhang ... 330 pages - Publisher: Gulf Professional; (June, 2020) ... AmazonSIN: B08BFG7ZNF - Language: English.


Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today’s petroleum and reservoir engineer to optimize more complex developments.

Understand commonly used and recent progress on definitions, models, and solution methods used in reservoir simulation + World leading modeling and algorithms to study flow and transport behaviors in reservoirs, as well as the application of machine learning + Gain practical knowledge with hand-on trainings on modeling and simulation through well designed case studies and numerical examples.

Dirk P. Kroese; Zdravko I. Botev; Thomas Taimre; Radislav Vaisman .... 532 pages - Language: English - AmazonSIN: B081S6BQ2Y - Publisher: Chapman and Hall/CRC; (November, 2019).


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.

Steven W. Knox ... Language: English - AmazonSIN: B07BHYKL4V ... 516 pages - Publisher: Wiley; (March, 2018).


Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. 

This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods + Presents R source code which shows how to apply and interpret many of the techniques covered + Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions + Contains useful information for effectively communicating with clients.

David Foster ... 330 pages - ISBN-10: 1492041947 - ISBN-13: 978-1492041948 ... Publisher : O'Reilly Media; (July, 2019) - Language: English.

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos + Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation + Create recurrent generative models for text generation and learn how to improve the models using attention + Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting + Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN.

Giuseppe Bonaccorso ... 522 pages - AmazonSIN : B07CSLQGNC - Publisher : Packt Publishing; 2nd Edition (August, 2018) - Language: English.

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.

By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn: Study feature selection and the feature engineering process + Assess performance and error trade-offs for linear regression + Build a data model and understand how it works by using different types of algorithm + Learn to tune the parameters of Support Vector Machines (SVM) + Explore the concept of natural language processing (NLP) and recommendation systems + Create a machine learning architecture from scratch. Who this book is for: Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.

John D. Kelleher ... 296 pages - Publisher: The MIT Press (September, 2019) ... Language: English - ISBN-10: 0262537559 - ISBN-13: 978-0262537551.

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.

Noah Gift ... 272 pages - Publisher: Addison-Wesley Professional; (September, 2018) ... Language: English - ISBN-10: 0134863860 - ISBN-13: 978-0134863863.

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning: Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need + Quickly review all the Python you need to start building machine learning applications + Master the AI and ML tool chain and project lifecycle + Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn + Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems + Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services + Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more + Work with Microsoft Azure AI APIs + Walk through building six real-world AI applications, from start to finish.

Nilanjan Dey, Sanjeev Wagh, Parikshit N. Mahalle, Mohd. Shafi Pathan ... 243 pages - Publisher: CRC Press; (May, 2019) ... Language: English - AmazonSIN: B07S4BVPSL.

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results.

Key Features: Follows an algorithmic approach for data analysis in machine learning + Introduces machine learning methods in applications + Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics + Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets + Case studies are covered relating to human health, transportation and Internet applications

Naiyang Deng, Yingjie Tian, Chunhua Zhang ... 363 pages - Publisher: Chapman and Hall/CRC; (December, 2012) ... Language: English - AmazonSIN: B00OD4GXCI.

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations. To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.

Kieth A. Carlson, Jennifer R. Winquist ... 656 pages - Publisher: SAGE Publications, Inc; 2nd edition (February, 2017) ... Language: English - ISBN-10: 148337873X - ISBN-13: 978-1483378732.

An Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.

Ethem Alpaydin ... 640 pages - Publisher: Phi; 3rd edition (2015) ... Language: English - ISBN-10: 8120350782 - ISBN-13: 978-8120350786.

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.

Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

Anand J. Kulkarni, Suresh Chandra Satapathy ... 197 pages - Publisher: Springer; (November, 2019) ... Language: English - AmazonSIN: B0825P5H9C.

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.

Richard S. Sutton, Andrew G. Barto ... 532 pages - Publisher: A Bradford Book; 2nd edition (October, 2018) ... Language: English - AmazonSIN: B07JN1QFW5.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Eugene Charniak ... 192 pages - Publisher: The MIT Press; (January, 2019) ... Language: English - AmazonSIN: B07PGRZXN8.

This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.

Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Terrence J. Sejnowski ... 354 pages - Publisher: MIT Press; (2018) ... Language: English - ISBN-10: 9780262038034 - ISBN-13: 978-0262038034.

How deep learning -from Google Translate to driverless cars to personal cognitive assistants- is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong ... 398 pages - Publisher: Cambridge Univ. Press; (April, 2020) ... Language: English - AmazonSIN: B083M7DBP6.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Shai Shalev-Shwartz, Shai Ben-David ... 415 pages - Publisher: Cambridge Univ. Press; (May, 2014) ... Language: English - AmazonSIN: B00J8LQU8I.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Kevin P. Murphy ... 1104 pages - Publisher: The MIT Press; (August, 2012) ... Language: English - ISBN-10: 0262018020 - ISBN-13: 978-0262018029.

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.

Aileen Nielsen ... 505 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - Amazon SIN: B07Y5WSCV2.

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data + Undertake exploratory time series data analysis + Store temporal data + Simulate time series data + Generate and select features for a time series + Measure error + Forecast and classify time series with machine or deep learning + Evaluate accuracy and performance

Allen Downey ... 200 pages - Publisher: O'Reilly Media; 2nd edition (July, 2018) ... Language: English - ASIN: B07FFC87K8 by Amazon.

Complexity science uses computation to explore the physical and social sciences. In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations. In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform + Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines + Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata + Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism. Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

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