Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
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Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
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Author :
language : en
Publisher:
Release Date : 1999

Advanced Topics In Artificial Intelligence written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Artificial intelligence categories.




Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
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Author : Vladimír Mařík
language : en
Publisher: Springer
Release Date : 1992

Advanced Topics In Artificial Intelligence written by Vladimír Mařík and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.


"This volume contains the texts of 26 lectures and contributions to the program of the International Summer School on Advanced Topics in Artificial Intelligence held in Prague, Czechoslovakia, July 6-17, 1992. The summerschool was intended for (postgraduate) students, researchers and all those who want to learn about recent progress in both theoretical and applied AI. The papers in the volume are organized into nine parts: - Introduction - Logic and logic programming - Machine learning - Planning and scheduling - Uncertainty - Second generation expert systemsand knowledge engineering - Qualitative reasoning - Neurocomputing -Natural language and interfaces"--PUBLISHER'S WEBSITE.



Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
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Author : Rolf T. Nossum
language : en
Publisher:
Release Date : 2014-01-15

Advanced Topics In Artificial Intelligence written by Rolf T. Nossum and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.




Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
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Author : Norman Foo
language : en
Publisher:
Release Date : 2014-01-15

Advanced Topics In Artificial Intelligence written by Norman Foo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.




Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
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Author : Rolf T. Nossum
language : en
Publisher: Springer Science & Business Media
Release Date : 1988-12-28

Advanced Topics In Artificial Intelligence written by Rolf T. Nossum and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-12-28 with Computers categories.


Organized by: European Coordinating Committee for AI (ECCAI)



Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
DOWNLOAD eBooks

Author : Vladimír Mařík
language : en
Publisher: Springer
Release Date : 1992

Advanced Topics In Artificial Intelligence written by Vladimír Mařík and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.


"This volume contains the texts of 26 lectures and contributions to the program of the International Summer School on Advanced Topics in Artificial Intelligence held in Prague, Czechoslovakia, July 6-17, 1992. The summerschool was intended for (postgraduate) students, researchers and all those who want to learn about recent progress in both theoretical and applied AI. The papers in the volume are organized into nine parts: - Introduction - Logic and logic programming - Machine learning - Planning and scheduling - Uncertainty - Second generation expert systemsand knowledge engineering - Qualitative reasoning - Neurocomputing -Natural language and interfaces"--PUBLISHER'S WEBSITE.



Advanced Topics In Artificial Intelligence


Advanced Topics In Artificial Intelligence
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Author : Norman Foo
language : en
Publisher: Springer
Release Date : 2007-12-07

Advanced Topics In Artificial Intelligence written by Norman Foo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-07 with Computers categories.


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.



Probabilistic Machine Learning


Probabilistic Machine Learning
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2022-03-01

Probabilistic Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-01 with Computers categories.


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.



Advanced Topics In Computer Vision


Advanced Topics In Computer Vision
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Author : Giovanni Maria Farinella
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-09-24

Advanced Topics In Computer Vision written by Giovanni Maria Farinella and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-24 with Computers categories.


This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. The text provides an overview of challenging areas and descriptions of novel algorithms. Features: investigates visual features, trajectory features, and stereo matching; reviews the main challenges of semi-supervised object recognition, and a novel method for human action categorization; presents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimization; examines solutions to the co-recognition problem, and distance-based classifiers for large-scale image classification; describes how the four-color theorem can be used for solving MRF problems; introduces a Bayesian generative model for understanding indoor environments, and a boosting approach for generalizing the k-NN rule; discusses the issue of scene-specific object detection, and an approach for making temporal super resolution video.



Probabilistic Machine Learning


Probabilistic Machine Learning
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Author : Kevin P. Murphy
language : en
Publisher: MIT Press
Release Date : 2023-08-15

Probabilistic Machine Learning written by Kevin P. Murphy and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-15 with Computers categories.


An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning. Covers generation of high dimensional outputs, such as images, text, and graphs Discusses methods for discovering insights about data, based on latent variable models Considers training and testing under different distributions Explores how to use probabilistic models and inference for causal inference and decision making Features online Python code accompaniment