{"product_id":"machine-learning-and-the-city-isbn-9781119749639","title":"Machine Learning and the City","description":"\u003cb\u003eMachine Learning and the City\u003c\/b\u003e \u003cp\u003e\u003cb\u003eExplore the applications of machine learning and artificial intelligence to the built environment\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning and the City: Applications in Architecture and Urban Design\u003c\/i\u003e delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning. \u003c\/p\u003e\u003cp\u003eEach section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eAn introduction to the probabilistic logic that underpins machine learning\u003c\/li\u003e \u003cli\u003eComprehensive explorations of the applications of machine learning and artificial intelligence to urban environments\u003c\/li\u003e \u003cli\u003ePractical discussions of the consequences of applied machine learning and the future of urban design\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for designers approaching machine learning and AI for the first time, \u003ci\u003eMachine Learning and the City: Applications in Architecture and Urban Design\u003c\/i\u003e will also earn a place in the libraries of urban planners and engineers involved in urban design. \u003c\/p\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgements xv\u003c\/p\u003e \u003cp\u003eIntroduction xvi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection I Urban Complexity 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Urban Complexity 3\u003cbr\u003e\u003ci\u003eSean Hanna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Emergence and Universal Computation 15\u003cbr\u003e\u003ci\u003eCassey Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Fractals and Geography 31\u003cbr\u003e\u003ci\u003ePierre Frankhauser and Denise Pumain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 1 Emergence and Urban Analysis 57\u003cbr\u003e\u003ci\u003eLjubomir Jankovic\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 2 The Evolution and Complexity of Urban Street Networks 63\u003cbr\u003e\u003ci\u003eNahid Mohajeri and Agust Gudmundsson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection II Machines that Think 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4 Artificial Intelligence, Logic, and Formalising Common Sense 71\u003cbr\u003e\u003ci\u003eJohn McCarthy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Defining Artificial Intelligence 91\u003cbr\u003e\u003ci\u003eDavid B. Fogel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 AI: From Copy of Human Brain to Independent Learner 121\u003cbr\u003e\u003ci\u003eShelly Fan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 The History of Machine Learning and Its Convergent Trajectory Towards AI 129\u003cbr\u003e\u003ci\u003eKeith D. Foote\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Machine Behaviour 143\u003cbr\u003e\u003ci\u003eIyad Rahwan, Manuel Cebrian, Nick Obradovich, Josh Bongard, Jean-François Bonnefon, \u003c\/i\u003e\u003ci\u003eCynthia Breazeal, Jacob W. Crandall, Nicholas A. Christakis, Iain D. Couzin, Matthew O. \u003c\/i\u003e\u003ci\u003eJackson, Nicholas R. Jennings, Ece Kamar, Isabel M. Kloumann, Hugo Larochelle, David \u003c\/i\u003e\u003ci\u003eLazer, Richard McElreath, Alan Mislove, David C. Parkes, Alex ‘Sandy’ Pentland, Margaret \u003c\/i\u003e\u003ci\u003eE. Roberts, Azim Shariff, Joshua B. Tenenbaum, and Michael Wellman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 3 Plan Generation from Program Graph 167\u003cbr\u003e\u003ci\u003eAo Li, Runjia Tian, Xiaoshi Wang, and Yueheng Lu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 4 Self-organising Floor Plans in Care Homes 171\u003cbr\u003e\u003ci\u003eSilvio Carta, Stephanie St. Loe, Tommaso Turchi, and Joel Simon\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 5 N\u003csup\u003e2\u003c\/sup\u003eP\u003csup\u003e2\u003c\/sup\u003e – Neural Networks and Public Places 177\u003cbr\u003e\u003ci\u003eRoberto Bottazzi, Tasos Varoudis, Piyush Prajapati, and Xi Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 6 Urban Fictions 183\u003cbr\u003e\u003ci\u003eMatias del Campo, Sandra Manninger, and Alexandra Carlson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 7 Latent Typologies: Architecture in Latent Space 189\u003cbr\u003e\u003ci\u003eStanislas Chaillou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 8 Enabling Alternative Architectures 193\u003cbr\u003e\u003ci\u003eNate Peters\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 9 Distant Readings of Architecture: A Machine View of the City 201\u003cbr\u003e\u003ci\u003eAndrew Witt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection III How Machines Learn 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9 What Is Machine Learning? 209\u003cbr\u003e\u003ci\u003eJason Bell\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Machine Learning: An Applied Mathematics Introduction 217\u003cbr\u003e\u003ci\u003ePaul Wilmott\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Machine Learning for Urban Computing 249\u003cbr\u003e\u003ci\u003eBilgeçağ Aydoğdu and Albert Ali Salah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Autonomous Artificial Intelligent Agents 263\u003cbr\u003e\u003ci\u003eIaroslav Omelianenko\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 10 Machine Learning for Spatial and Visual Connectivity 287\u003cbr\u003e\u003ci\u003eSherif Tarabishy, Stamatios Psarras, Marcin Kosicki, and Martha Tsigkari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 11 Navigating Indoor Spaces Using Machine Learning: Train Stations in Paris 293\u003cbr\u003e\u003ci\u003eZhoutong Wang, Qianhui Liang, Fabio Duarte, Fan Zhang, Louis Charron, Lenna Johnsen, Bill Cai, and Carlo Ratti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 12 Evolutionary Design Optimisation of Traffic Signals Applied to Quito City 297\u003cbr\u003e\u003ci\u003eRolando Armas, Hernán Aguirre, Fabio Daolio, and Kiyoshi Tanaka\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 13 Constructing Agency: Self-directed Robotic Environments 303\u003cbr\u003e\u003ci\u003ePatrik Schumacher\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection IV Application to the City 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13 Code and the Transduction of Space 311\u003cbr\u003e\u003ci\u003eMartin Dodge and Rob Kitchin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Augmented Reality in Urban Places: Contested Content and the Duplicity of Code 341\u003cbr\u003e\u003ci\u003eMark Graham, Matthew Zook, and Andrew Boulton\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Spatial Data in Urban Informatics: Contentions of the Software-sorted City 367\u003cbr\u003e\u003ci\u003eMarcus Foth, Fahame Emamjome, Peta Mitchell, and Markus Rittenbruch\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 Urban Morphology Meets Deep Learning: Exploring Urban Forms in One Million Cities, Towns, and Villages Across the Planet 379\u003cbr\u003e\u003ci\u003eVahid Moosavi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 Computational Urban Design: Methods and Case Studies 393\u003cbr\u003e\u003ci\u003eSnoweria Zhang and Luc Wilson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 Indexical Cities: Personal City Models with Data as Infrastructure 409\u003cbr\u003e\u003ci\u003eDiana Alvarez-Marin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19 Machine Learning, Artificial Intelligence, and Urban Assemblages 445\u003cbr\u003eSerjoscha Düring, Reinhard Koenig, Nariddh Khean, Diellza Elshani, Theodoros Galanos, and Angelos Chronis\u003c\/p\u003e \u003cp\u003e20 Making a Smart City Legible 453\u003cbr\u003e\u003ci\u003eFranziska Pilling, Haider Ali Akmal, Joseph Lindley, and Paul Coulton\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 14 A Tale of Many Cities: Universal Patterns in Human Urban Mobility 467\u003cbr\u003e\u003ci\u003eAnastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, and Cecilia Mascolo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 15 Using Cellular Automata for Parking Recommendations in Smart Environments 473\u003cbr\u003e\u003ci\u003eGwo-Jiun Horng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 16 Gan Hadid 477\u003cbr\u003e\u003ci\u003eSean Wallish\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 17 Collective Design for Collective Living 483\u003cbr\u003e\u003ci\u003eElizabeth Christoforetti and Romy El Sayah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 18 Architectural Machine Translation 489\u003cbr\u003e\u003ci\u003eErik Swahn\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 19 Large-scale Evaluation of the Urban Street View with Deep Learning Method 495\u003cbr\u003e\u003ci\u003eHui Wang, Elisabete A. Silva, and Lun Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 20 Urban Portraits 501\u003cbr\u003e\u003ci\u003eJose Luis García del Castillo y López\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 21 ML-City 507\u003cbr\u003e\u003ci\u003eBenjamin Ennemoser\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 22 Imaging Place Using Generative Adversarial Networks (GAN Loci) 513\u003cbr\u003e\u003ci\u003eKyle Steinfeld\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 23 Urban Forestry Science 517\u003cbr\u003e\u003ci\u003eIacopo Testi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection V Machine Learning and Humans 521\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21 Ten Simple Rules for Responsible Big Data Research 523\u003cbr\u003e\u003ci\u003eMatthew Zook, Solon Barocas, Danah Boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, and Frank Pasquale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22 A Unified Framework of Five Principles for AI in Society 535\u003cbr\u003e\u003ci\u003eLuciano Floridi and Josh Cowls\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23 The Big Data Divide and Its Consequences 547\u003cbr\u003e\u003ci\u003eMatthew T. McCarthy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 Design Fiction: A Short Essay on Design, Science, Fact, and Fiction 561\u003cbr\u003e\u003ci\u003eJulian Bleecker\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 Superintelligence and Singularity 579\u003cbr\u003e\u003ci\u003eRay Kurzweil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26 The Social Life of Robots: The Politics of Algorithms, Governance, and Sovereignty 603\u003cbr\u003e\u003ci\u003eVincent J. Del Casino Jr, Lily House-Peters, Jeremy W. Crampton, and Hannes Gerhardt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 24 Experiments in Synthetic Data 615\u003cbr\u003e\u003ci\u003eForensic Architecture\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 25 Emotional AI in Cities: Cross-cultural Lessons from the UK and Japan on Designing for an Ethical Life 621\u003cbr\u003e\u003ci\u003eVian Bakir, Nader Ghotbi, Tung Manh Ho, Alexander Laffer, Peter Mantello, Andrew McStay, Diana Miranda, Hiroshi Miyashita, Lena Podoletz, Hiromi Tanaka, and Lachlan Urquhart\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 26 Decoding Urban Inequality: The Applications of Machine Learning for Mapping Inequality in Cities of the Global South 625\u003cbr\u003e\u003ci\u003eKadeem Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 27 Amsterdam 2040 631\u003cbr\u003e\u003ci\u003eMaria Luce Lupetti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eProject 28 Committee of Infrastructure 635\u003cbr\u003e\u003ci\u003eJason Shun Wong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 639\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eSilvio Carta\u003c\/b\u003e is an architect and Associate Professor at the University of Hertfordshire, UK. His research interests include digital architecture, data-driven approaches and computational design. Silvio is the author of \u003ci\u003eBig Data, Code and the Discrete City. Shaping Public Realms\u003c\/i\u003e (Routledge 2019).   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eExplore the applications of machine learning and artificial intelligence to the built environment\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning and the City: Applications in Architecture and Urban Design\u003c\/i\u003e delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning. \u003c\/p\u003e\u003cp\u003eEach section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eAn introduction to the probabilistic logic that underpins machine learning\u003c\/li\u003e \u003cli\u003eComprehensive explorations of the applications of machine learning and artificial intelligence to urban environments\u003c\/li\u003e \u003cli\u003ePractical discussions of the consequences of applied machine learning and the future of urban design\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for designers approaching machine learning and AI for the first time, \u003ci\u003eMachine Learning and the City: Applications in Architecture and Urban Design\u003c\/i\u003e will also earn a place in the libraries of urban planners and engineers involved in urban design.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47989548024037,"sku":"NP9781119749639","price":115.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119749639.jpg?v=1761784550","url":"https:\/\/k12savings.com\/es\/products\/machine-learning-and-the-city-isbn-9781119749639","provider":"K12savings","version":"1.0","type":"link"}