02778nam 2200565 450 9910282240403321201807112-7606-3096-X979-1-03-650465-52-7606-2656-310.4000/books.pum.21882(CKB)3710000000346823(MiAaPQ)EBC4750129(FrMaCLE)OB-pum-21882(oapen)https://directory.doabooks.org/handle/20.500.12854/44373(PPN)233380035(EXLCZ)99371000000034682320170130h20102010 uy 0freurcnu||||||||rdacontentrdamediardacarrierLa culture comme refus de l'économisme écrits de Marcel Rioux /Textes choisis et présentés par Jacques Hamel, Julien Forgues Lecavalier et Marcel Fournier ; avec la collaboration de Gabriel GagnonPresses de l’Université de Montréal2000Montréal, [Quebećbec, Canada] :Les Presses de l'Université de Montréal,2010.©20101 online resource (586 pages)2-7606-2232-0 Includes bibliographical references.Marcel Rioux (1919-1992) a consacré une large part de son œuvre à envisager la société québécoise sous l’angle de la culture. C’est selon lui par cette voie que le Québec a pu se concevoir comme société nationale, susceptible de devenir le pays qu’il appelait de ses vœux. Publiés entre 1957 et 1987, les textes réunis dans cet ouvrage offrent une vue d’ensemble de la pensée d’un grand témoin de son temps. Durant ces trente années, Rioux a observé sur le vif la mutation de la culture, tant à l’échelle régionale (on pense à sa monographie sur Belle-Anse) qu’au sein des groupes sociaux (ses études sur la jeunesse) ou des institutions (ses travaux et propos sur l’éducation). D’une actualité surprenante, les écrits de Marcel Rioux peuvent être lus comme autant de manifestes contre le discours économiste qui prétend imposer une vision marchande de la culture.Québec (Province)Social conditions21st centuryjeunesseéconomismechangement socialcultureidéologie306.09714Julien Forgues Lecavalierauth1355478Hamel JacquesLecavalier Julien ForguesFournier Marcel1945-Gagnon GabrielMiAaPQMiAaPQMiAaPQBOOK9910282240403321La culture comme refus de l'économisme3359595UNINA03528nam 22005895 450 991074119470332120251009085027.09783031316364303131636310.1007/978-3-031-31636-4(CKB)27965612600041(DE-He213)978-3-031-31636-4(PPN)272272647(MiAaPQ)EBC30682611(Au-PeEL)EBL30682611(MiAaPQ)EBC30766880(Au-PeEL)EBL30766880(EXLCZ)992796561260004120230809d2023 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierData Driven Model Learning for Engineers With Applications to Univariate Time Series /by Guillaume Mercère1st ed. 2023.Cham :Springer Nature Switzerland :Imprint: Springer,2023.1 online resource (X, 212 p. 93 illus., 54 illus. in color.)9783031316357 The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.Time-series analysisMachine learningStatisticsTime Series AnalysisStatistical LearningStatistics in Engineering, Physics, Computer Science, Chemistry and Earth SciencesTime-series analysis.Machine learning.Statistics.Time Series Analysis.Statistical Learning.Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.519.55620.00151955Mercère Guillaumeauthttp://id.loc.gov/vocabulary/relators/aut1428817MiAaPQMiAaPQMiAaPQBOOK9910741194703321Data Driven Model Learning for Engineers3566199UNINA