Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advanced Topics in Artificial Intelligence written by Norman Foo and published by Springer Science & Business Media. This book was released on 1999-11-26 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th Australian Joint Conference on Artificial Intelligence, AI'99, held in Sydney, Australia in December 1999. The 39 revised full papers presented together with 15 posters were carefully reviewed and selected from more than 120 submissions. The book is divided in topical sections on machine learning, neural nets, knowledge representation, natural language processing, belief revision, adaptive algorithms, automated reasonning, neural learning, heuristics, and applications

Download or read book Advanced Topics in Artificial Intelligence written by Rolf T. Nossum and published by Springer Science & Business Media. This book was released on 1988-12-28 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organized by: European Coordinating Committee for AI (ECCAI)

Download or read book Advanced Topics in Artificial Intelligence written by John K. Slaney and published by Springer Science & Business Media. This book was released on 1998-10-07 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th Australian Joint Conference on Artificial Intelligence, AI'97, held in Perth, Australia, in November/December 1997. The volume presents 48 revised full papers selected from a total of 143 submissions. Also included are three keynote talks and one invited paper. The book is divided into topical sections on constraint satisfaction and scheduling, computer vision, distributed AI, evolutionary computing, knowledge-based systems, knowledge representation and reasoning, learning and machine vision, machine learning, NLP and user modeling, neural networks, robotics and machine recognition, and temporal qualitative reasoning.

Download or read book Advances in Artificial Intelligence based Technologies written by Maria Virvou and published by Springer Nature. This book was released on 2021-10-02 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the 4th Industrial Revolution ongoing and human societal organization being restructured into, so-called, “Society 5.0”, the field of Artificial Intelligence and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Artificial Intelligence components, they become better and more efficient at performing tasks. Consequently, Artificial Intelligence stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Artificial Intelligence Theory, Tools and Methodologies as well as Artificial Intelligence-based Applications and Services. The book consists of an editorial note and an additional eleven (11) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into three parts, namely (i) Advances in Artificial Intelligence Tools and Methodologies, (ii) Advances in Artificial Intelligence-based Applications and Services, and (iii) Theoretical Advances in Computation and System Modeling. This research book is directed towards professors, researchers, scientists, engineers and students in Artificial Intelligence-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Artificial Intelligence-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.

Download or read book Advanced Topics in Artificial Intelligence written by Rolf T. Nossum and published by Springer Science & Business Media. This book was released on 1988-12-28 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Organized by: European Coordinating Committee for AI (ECCAI)

Download or read book Advanced Topics in Artificial Intelligence written by Norman Foo and published by Springer. This book was released on 2007-12-07 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 12th Australian Joint Conference on Artificial Intelligence (AI'QQ) held in Sydney, Australia, 6-10 December 1999, is the latest in a series of annual re gional meetings at which advances in artificial intelligence are reported. This series now attracts many international papers, and indeed the constitution of the program committee reflects this geographical diversity. Besides the usual tutorials and workshops, this year the conference included a companion sympo sium at which papers on industrial appUcations were presented. The symposium papers have been published in a separate volume edited by Eric Tsui. Ar99 is organized by the University of New South Wales, and sponsored by the Aus tralian Computer Society, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Computer Sciences Corporation, the KRRU group at Griffith University, the Australian Artificial Intelligence Institute, and Neuron- Works Ltd. Ar99 received over 120 conference paper submissions, of which about o- third were from outside Australia. Prom these, 39 were accepted for regular presentation, and a further 15 for poster display. These proceedings contain the full regular papers and extended summaries of the poster papers. All papers were refereed, mostly by two or three reviewers selected by members of the program committee, and a list of these reviewers appears later. The technical program comprised two days of workshops and tutorials, fol lowed by three days of conference and symposium plenary and paper sessions.

Download or read book Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2012-08-24 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances in Deep Learning written by M. Arif Wani and published by Springer. This book was released on 2019-03-14 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.

Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Download or read book Universal Artificial Intelligence written by Marcus Hutter and published by Springer Science & Business Media. This book was released on 2005-12-29 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob lems, pattern recognition, classification, learning, induction, deduction, build ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advances in Artificial Intelligence written by Canadian Society for Computational Studies of Intelligence. Conference and published by Springer Science & Business Media. This book was released on 1998-05-27 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'98, held in Vancouver, BC, Canada in June 1998. The 28 revised full papers presented together with 10 extended abstracts were carefully reviewed and selected from a total of more than twice as many submissions. The book is divided in topical sections on planning, constraints, search and databases; applications; genetic algorithms; learning and natural language; reasoning; uncertainty; and learning.

Download or read book Advances in Artificial Intelligence written by Howard J. Hamilton and published by Springer. This book was released on 2003-06-26 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000, held in Montreal, Quebec, Canada, in May 2000. The 25 revised full papers presented together with 12 10-page posters were carefully reviewed and selected from more than 70 submissions. The papers are organized in topical sections on games and constraint satisfaction; natural language processing; knowledge representation; AI applications; machine learning and data mining; planning, theorem proving, and artificial life; and neural networks.

Download or read book Advances in Artificial Intelligence written by Jose A. Lozano and published by Springer Science & Business Media. This book was released on 2011-10-28 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2009, held in La Laguna, Canary Islands, Spain, in November 2011. The 50 revised full papers presented were carefully selected from 149 submissions. The papers are organized in topical sections on agent-based and multi-agent systems; machine learning; knowledge representation, logic, search and planning; multidisciplinary topics and applications; vision and robotics; soft computing; Web intelligence and information retrieval.

Download or read book Probability for Statistics and Machine Learning written by Anirban DasGupta and published by Springer Science & Business Media. This book was released on 2011-05-17 with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

Download or read book Artificial Intelligence and Machine Learning Fundamentals written by Zsolt Nagy and published by Packt Publishing Ltd. This book was released on 2018-12-12 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).