James a Goulet (17 results)

Condition: New. pp. 304.

Paperback. Condition: new. Paperback. An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises.This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws.The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment. An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New.

Paperback. Condition: New.

Condition: New. pp. 304.

Condition: As New. Unread book in perfect condition.

Condition: New. pp. 304.

Encuadernación de tapa dura. Condition: Bien. Panamericana, 2006. Ciencias de la Salud, Naturales y Divulgación Científica. Especialidades. Medicina. Patología general. Medicina clínica. Terapéutica. Profusamente ilustrado. 100 páginas aprox. 25 x 18. Tapa dura con sobrecubierta de editorial ilustrada. Sin subrayados. Perfecto estado de conservación. ISBN: 8479033037.…

Condition: New. 2020. Illustrated. Paperback. . . . . .

Paperback. Condition: Brand New. 269 pages. 9.75x8.00x0.75 inches. In Stock.

Condition: New. 2020. Illustrated. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condition: New.

Hardcover. Condition: Very Good. 2012 Cambridge University Press hardcover edition. Unread copy in very good condition. From the cover: Edgar Allan Poe mastered a variety of literary forms over the course of his brief and turbulent career. As a storyteller, Poe defied convention by creating Gothic tales of mystery, horror and suspense that remain widely popular today. This collection demonstrates how Poe's experience of early nineteenth-century American life fueled his iconoclasm and shaped his literary legacy. Rather than provide critical explications of his writings, each essay explores one aspect of Poe's immediate environment, using pertinent writings - verse, fiction, reviews and essays - to suit. Examining his geographical, social and literary contexts, as well as those created by the publishing industry and advances in science and technology, the essays paint an unprecedented portrait of Poe's life and times. Written for a wide audience, the collection will offer scholars and students of American literature, historians and general readers new insight into Poe's rich and complex work. Contents: Part One. Geographical Contexts 1. Great Britain Christopher Gair 2. The South James Hutchisson 3. The American West Nathaniel Lewis 4. The Sea David Dowling 5. France Andrea Goulet 6. The Near East Travis Montgomery 7. The Polar Regions Mark Canada Part Two. Social Contexts 8. The Urban Environment Bran Nichol 9. Curiosity Lindsey Hursh 10. Alcohol, Addiction, and Rehabilitation Paul Fisher 11. Fashion, Furnishings, and Style Patricia A. Cunningham 12. The American Stage Philip Edward Phillips 13. Lions and Bluestockings Anne Boyd Rioux 14. Slavery and Abolitionism Paul Christian Jones 15. The Cult of Mourning Therese M. Rizzo Part Three. The Contexts of Publishing 16. The Literary Profession John Evelev 17. Magazines Jeffrey Andrew Weinstock 18. Gift Books Kathryn R. Shinn 19. Literary Piracy Michael J. Everton 20. The Art of Reviewing Jonathan Hartmann 21. The Politics of Publishing Amy Branam Part Four. Literary Contexts 22. Ancient Classics Gregory Hays 23. Rabelais and Lesage Lois Davis Vines 24. The Gothic Movement Alan Brown 25. Byron Chris Beyers 26. Folk Narrative Katherine Kim 27. Transcendentalism Heidi Silcox 28. Charles Dickens Tara Moore 29. Nathaniel Hawthorne and the Art of the Tale Meghan A. Freeman Part Five. Scientific and Pseudoscientific Contexts 30. Phrenology Brett Zimmerman 31. Photography Satwik Dasgupta 32. Mesmerism Bruce Mills 33. Architecture Alvin Holm 34. The Heritage of Fiction Science Peter Swirski 35. Cosmology and Cosmogony Jonathan Taylor 36. Forensic Science Benjamin F. Fisher 37. Technology John Tresch.…

Paperback. Condition: new. Paperback. An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises.This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws.The book presents key approaches in the three subfields of probabilistic machine learning- supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment. An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: New.

Condition: As New. Unread copy in mint condition.

Condition: New. Brand New.