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3.032 Mechanical Behavior of Materials (MIT) 3.032 Mechanical Behavior of Materials (MIT)

Description

Here we will learn about the mechanical behavior of structures and materials, from the continuum description of properties to the atomistic and molecular mechanisms that confer those properties to all materials. We will cover elastic and plastic deformation, creep, and fracture of materials including crystalline and amorphous metals, ceramics, and (bio)polymers, and will focus on the design and processing of materials from the atomic to the macroscale to achieve desired mechanical behavior. Integrated laboratories provide the opportunity to explore these concepts through hands-on experiments including instrumentation of pressure vessels, visualization of atomistic deformation in bubble rafts, nanoindentation, and uniaxial mechanical testing, as well as writing assignments to communicate th Here we will learn about the mechanical behavior of structures and materials, from the continuum description of properties to the atomistic and molecular mechanisms that confer those properties to all materials. We will cover elastic and plastic deformation, creep, and fracture of materials including crystalline and amorphous metals, ceramics, and (bio)polymers, and will focus on the design and processing of materials from the atomic to the macroscale to achieve desired mechanical behavior. Integrated laboratories provide the opportunity to explore these concepts through hands-on experiments including instrumentation of pressure vessels, visualization of atomistic deformation in bubble rafts, nanoindentation, and uniaxial mechanical testing, as well as writing assignments to communicate thSubjects

Basic concepts of solid mechanics and mechanical behavior of materials | Basic concepts of solid mechanics and mechanical behavior of materials | stress-strain relationships | stress-strain relationships | stress transformation | stress transformation | elasticity | elasticity | plasticity and fracture. Case studies include materials selection for bicycle frames | plasticity and fracture. Case studies include materials selection for bicycle frames | stress shielding in biomedical implants; residual stresses in thin films; and ancient materials. Lab experiments and demonstrations give hands-on experience of the physical concepts at a variety of length scales. Use of facilities for measuring mechanical properties including standard mechanical tests | stress shielding in biomedical implants; residual stresses in thin films; and ancient materials. Lab experiments and demonstrations give hands-on experience of the physical concepts at a variety of length scales. Use of facilities for measuring mechanical properties including standard mechanical tests | bubble raft models | bubble raft models | atomic force microscopy and nanoindentation. | atomic force microscopy and nanoindentation. | plasticity and fracture | plasticity and fracture | Case studies | Case studies | materials selection | materials selection | bicycle frames | bicycle frames | stress shielding in biomedical implants | stress shielding in biomedical implants | residual stresses in thin films | residual stresses in thin films | ancient materials | ancient materials | standard mechanical tests | standard mechanical tests | solid mechanics | solid mechanics | mechanical behavior of materials | mechanical behavior of materialsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata14.385 Nonlinear Econometric Analysis (MIT) 14.385 Nonlinear Econometric Analysis (MIT)

Description

This course presents micro-econometric models, including large sample theory for estimation and hypothesis testing, generalized method of moments (GMM), estimation of censored and truncated specifications, quantile regression, structural estimation, nonparametric and semiparametric estimation, treatment effects, panel data, bootstrapping, simulation methods, and Bayesian methods. The methods are illustrated with economic applications. This course presents micro-econometric models, including large sample theory for estimation and hypothesis testing, generalized method of moments (GMM), estimation of censored and truncated specifications, quantile regression, structural estimation, nonparametric and semiparametric estimation, treatment effects, panel data, bootstrapping, simulation methods, and Bayesian methods. The methods are illustrated with economic applications.Subjects

nonlinear | nonlinear | econometric | econometric | analysis | analysis | generalized method of moments | generalized method of moments | GMM | GMM | maximum likelihood estimation | maximum likelihood estimation | MLE | MLE | minimum distance | minimum distance | extremum | extremum | large sample theory | large sample theory | asymptotic theory | asymptotic theory | discrete choice | discrete choice | censoring | censoring | sample selection | sample selection | bootstrap | bootstrap | subsampling | subsampling | finite-sample methods | finite-sample methods | quantile regression | quantile regression | QR | QR | distributional methods | distributional methods | Bayesian methods | Bayesian methods | quasi-Bayesian methods | quasi-Bayesian methods | bounds | bounds | partial identification | partial identification | weak instruments | weak instruments | many instruments | many instruments | instrumental variables | instrumental variables | nonparametric estimation | nonparametric estimation | semiparametric estimation | semiparametric estimation | treatment effects | treatment effects | nonlinear models | nonlinear models | panel data | panel data | economic modeling | economic modelingLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata15.060 Data, Models, and Decisions (MIT) 15.060 Data, Models, and Decisions (MIT)

Description

This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice. This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice.Subjects

decision analysis | decision analysis | discrete probability distributions | discrete probability distributions | continuous probability distributions | continuous probability distributions | normal probability distribution | normal probability distribution | statistical sampling | statistical sampling | regression models | regression models | linear optimization | linear optimization | nonlinear optimization | nonlinear optimization | discrete optimization | discrete optimizationLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadataMagnetism with an Experimental Focus (MIT) Magnetism with an Experimental Focus (MIT)

Description

This course is an introduction to electromagnetism and electrostatics. Topics include: electric charge, Coulomb's law, electric structure of matter, conductors and dielectrics, concepts of electrostatic field and potential, electrostatic energy, electric currents, magnetic fields, Ampere's law, magnetic materials, time-varying fields, Faraday's law of induction, basic electric circuits, electromagnetic waves, and Maxwell's equations. The course has an experimental focus, and includes several experiments that are intended to illustrate the concepts being studied. Acknowledgements Prof. Roland wishes to acknowledge that the structure and content of this course owe much to the contributions of Prof. Ambrogio Fasoli. This course is an introduction to electromagnetism and electrostatics. Topics include: electric charge, Coulomb's law, electric structure of matter, conductors and dielectrics, concepts of electrostatic field and potential, electrostatic energy, electric currents, magnetic fields, Ampere's law, magnetic materials, time-varying fields, Faraday's law of induction, basic electric circuits, electromagnetic waves, and Maxwell's equations. The course has an experimental focus, and includes several experiments that are intended to illustrate the concepts being studied. Acknowledgements Prof. Roland wishes to acknowledge that the structure and content of this course owe much to the contributions of Prof. Ambrogio Fasoli.Subjects

Electromagnetism | Electromagnetism | electrostatics | electrostatics | electric charge | electric charge | Coulomb's law | Coulomb's law | electric structure of matter | electric structure of matter | conductors | conductors | dielectrics | dielectrics | electrostatic field | electrostatic field | electrostatic potential | electrostatic potential | electrostatic energy | electrostatic energy | electric current | electric current | magnetic field | magnetic field | Ampere's law | Ampere's law | magnetic | magnetic | electric | electric | time-varying fields | time-varying fields | Faraday's law | Faraday's law | induction | induction | circuits | circuits | electromagnetic waves | electromagnetic waves | Maxwell's equations | Maxwell's equations | 8.02 | 8.02License

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This seminar will explore the difficulties of getting agreement on global definitions of sustainability; in particularly building international support for efforts to combat climate change created by greenhouse gas emissions as well as other international resource management efforts. We will focus on possible changes in the way global environmental agreements are formulated and implemented, especially on ways of shifting from the current "pollution control" approach to combating climate change to a more comprehensive strategy for taking advantage of sustainable development opportunities. This seminar will explore the difficulties of getting agreement on global definitions of sustainability; in particularly building international support for efforts to combat climate change created by greenhouse gas emissions as well as other international resource management efforts. We will focus on possible changes in the way global environmental agreements are formulated and implemented, especially on ways of shifting from the current "pollution control" approach to combating climate change to a more comprehensive strategy for taking advantage of sustainable development opportunities.Subjects

sustainability | sustainability | managing common resources | managing common resources | transboundary pollution control | transboundary pollution control | economic development | economic development | social development | social development | environmental resource protection | environmental resource protection | environmental protection standardsinternational negotiations | environmental protection standardsinternational negotiations | multi-lateral negotiations | multi-lateral negotiations | representation | representation | voting | voting | issue linkage | issue linkage | balancing science and politics | balancing science and politics | Climate Change Convention | Climate Change ConventionLicense

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See all metadata1.018J Fundamentals of Ecology (MIT) 1.018J Fundamentals of Ecology (MIT)

Description

This is a basic subject in ecology that seeks to improve the understanding of the flow of energy and materials through ecosystems and the regulation of the distribution and abundance of organisms. The course covers productivity and biogeochemical cycles in ecosystems, trophic dynamics, community structure and stability, competition and predation, evolution and natural selection, population growth and physiological ecology. There is particular emphasis placed on aquatic systems. This is a basic subject in ecology that seeks to improve the understanding of the flow of energy and materials through ecosystems and the regulation of the distribution and abundance of organisms. The course covers productivity and biogeochemical cycles in ecosystems, trophic dynamics, community structure and stability, competition and predation, evolution and natural selection, population growth and physiological ecology. There is particular emphasis placed on aquatic systems.Subjects

ecology | ecology | flow of energy | flow of energy | flow of materials | flow of materials | ecosystems | ecosystems | distribution and abundance of organisms | distribution and abundance of organisms | productivity cycles | productivity cycles | biogeochemical cycles | biogeochemical cycles | trophic dynamics | trophic dynamics | community structure and stability | community structure and stability | competition and predation | competition and predation | evolution and natural selection | evolution and natural selection | population growth | population growth | physiological ecology | physiological ecology | aquatic systems | aquatic systems | community structure | community structure | community stability | community stability | competition | competition | predation | predation | distribution | distribution | organisms | organisms | evolution | evolution | natural selection | natural selection | energy flow | energy flow | 1.018 | 1.018 | 7.30 | 7.30License

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Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects. Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.Subjects

prediction | prediction | machine learning | machine learning | statistics | statistics | data mining | data mining | algorithms | algorithms | statistical learning theory | statistical learning theory | Bayesian analysis | Bayesian analysis | top 10 algorithms | top 10 algorithms | support vector machines | support vector machines | boosting | boostingLicense

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In this course we shall develop theoretical methods suitable for the description of the many-body phenomena, such as Hamiltonian second-quantized operator formalism, Greens functions, path integral, functional integral, and the quantum kinetic equation. The concepts to be introduced include, but are not limited to, the random phase approximation, the mean field theory (aka saddle-point, or semiclassical approximation), the tunneling dynamics in imaginary time, instantons, Berry phase, coherent state path integral, renormalization group. In this course we shall develop theoretical methods suitable for the description of the many-body phenomena, such as Hamiltonian second-quantized operator formalism, Greens functions, path integral, functional integral, and the quantum kinetic equation. The concepts to be introduced include, but are not limited to, the random phase approximation, the mean field theory (aka saddle-point, or semiclassical approximation), the tunneling dynamics in imaginary time, instantons, Berry phase, coherent state path integral, renormalization group.Subjects

condensed matter systems | condensed matter systems | low-dimension magnetic and electronic systems | low-dimension magnetic and electronic systems | disorder and quantum transport | disorder and quantum transport | magnetic impurities | magnetic impurities | the Kondo problem | the Kondo problem | quantum spin systems | quantum spin systems | the Hubbard model | the Hubbard model | high temperature superconductors | high temperature superconductors | Bose Condensates | Bose Condensates | Quasiparticles | Quasiparticles | Collective Modes | Collective Modes | Superfluidity | Superfluidity | Vortices | Vortices | Fermi Gases | Fermi Gases | Fermi Liquids | Fermi Liquids | Collective Excitations | Collective Excitations | Cooper Pairing | Cooper Pairing | BCS Theory | BCS Theory | Off-diagonal Long-range Order | Off-diagonal Long-range Order | Superconductivity | Superconductivity | Atom Interacting | Atom Interacting | Optical Fields | Optical Fields | Lamb Shift | Lamb Shift | Casimir Effect | Casimir Effect | Dicke Superradiance | Dicke Superradiance | Quantum Transport | Quantum Transport | Wave Scattering | Wave Scattering | Disordered Media | Disordered Media | Localization | Localization | Tunneling | Tunneling | Instantons | Instantons | Macroscopic Quantum Systems | Macroscopic Quantum Systems | Coupling | Coupling | Thermal Bath | Thermal Bath | Spin-boson Model | Spin-boson Model | Kondo Effect | Kondo Effect | Spin Dynamics | Spin Dynamics | Gases Transport | Gases Transport | Solids Transport | Solids Transport | Cold Atoms | Cold Atoms | Optical Lattices | Optical Lattices | Quantum Theory | Quantum Theory | Photodetection | Photodetection | Electric Noise | Electric NoiseLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata17.50 Introduction to Comparative Politics (MIT) 17.50 Introduction to Comparative Politics (MIT)

Description

This class first offers some basic analytical frameworks - culture, social structure, and institutions - that you can use to examine a wide range of political outcomes. We then use these frameworks to understand (1) the relationship between democracy and economic development and (2) the relative centralization of political authority across countries. We will use theoretical arguments and a wide range of case studies to address several questions: Why are some countries democratic and others not? How does democracy affect economic development and political conflict? Why do some countries centralize power while others threaten to fall apart through secession and civil war? We will use examples from a wide range of countries including Argentina, Brazil, Germany, Iraq, Italy, Mexico, and the Un This class first offers some basic analytical frameworks - culture, social structure, and institutions - that you can use to examine a wide range of political outcomes. We then use these frameworks to understand (1) the relationship between democracy and economic development and (2) the relative centralization of political authority across countries. We will use theoretical arguments and a wide range of case studies to address several questions: Why are some countries democratic and others not? How does democracy affect economic development and political conflict? Why do some countries centralize power while others threaten to fall apart through secession and civil war? We will use examples from a wide range of countries including Argentina, Brazil, Germany, Iraq, Italy, Mexico, and the UnSubjects

Democracy | political institutions | economic development | political conflict | ethnic conflict | India | Weimar Germany | market-oriented reform | Brazil | corruption | Mexico | ethnic violence | Yugoslavia | post-Communist Russia | China | Democracy | political institutions | economic development | political conflict | ethnic conflict | India | Weimar Germany | market-oriented reform | Brazil | corruption | Mexico | ethnic violence | Yugoslavia | post-Communist Russia | China | Democracy | Democracy | political institutions | political institutions | economic development | economic development | political conflict | political conflict | ethnic conflict | ethnic conflict | India | India | Weimar Germany | Weimar Germany | market-oriented reform | market-oriented reform | Brazil | Brazil | corruption | corruption | Mexico | Mexico | ethnic violence | ethnic violence | Yugoslavia | Yugoslavia | post-Communist Russia | post-Communist Russia | China | ChinaLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This class covers a range of topics including hypertext, interactive cinema, games, installation art, and soundscapes. It examines the potential for dynamic narrative in traditional media like novels and films and as well as in computer-based stories and games. The course focuses on the creation of electronic stories and games using simple authoring systems and multimedia software tools. Students present and constructively critique one another's work in progress in a workshop setting aimed at expanding the representational powers of a new creative medium. This class covers a range of topics including hypertext, interactive cinema, games, installation art, and soundscapes. It examines the potential for dynamic narrative in traditional media like novels and films and as well as in computer-based stories and games. The course focuses on the creation of electronic stories and games using simple authoring systems and multimedia software tools. Students present and constructively critique one another's work in progress in a workshop setting aimed at expanding the representational powers of a new creative medium.Subjects

Theory | Theory | Practice | Practice | Non-linear | Non-linear | Interactive | Interactive | Narrative | Narrative | Film | Film | Writing | Writing | Games | Games | Web | Web | HTML | HTML | Multilinear | Multilinear | Story | Story | creative writing | creative writing | computers | computers | book-based narratives | book-based narratives | structure | structure | digression | digression | multiple points of view | multiple points of view | storyline | storyline | hypertexts | hypertexts | adventure games | adventure games | artificial intelligence programs | artificial intelligence programs | eliza | eliza | modeling | modeling | computer-based narratives | computer-based narratives | 21W.765 | 21W.765 | 21L.489 | 21L.489License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadataMotivation and factors affecting motivation Motivation and factors affecting motivation

Description

This free course explores the term 'motivation' and factors affecting motivation. This includes looking at the most influential theories of motivation that contribute to understanding the causes of motivation. The motivation of sports people and people working in sport and fitness environments are used to help understand the theories presented. First published on Tue, 16 Feb 2016 as Motivation and factors affecting motivation. To find out more visit The Open University's Openlearn website. Creative-Commons 2016 This free course explores the term 'motivation' and factors affecting motivation. This includes looking at the most influential theories of motivation that contribute to understanding the causes of motivation. The motivation of sports people and people working in sport and fitness environments are used to help understand the theories presented. First published on Tue, 16 Feb 2016 as Motivation and factors affecting motivation. To find out more visit The Open University's Openlearn website. Creative-Commons 2016Subjects

Health | Sports & Psychology | Health | Sports & Psychology | Sport and Fitness | Sport and Fitness | E113_1 | E113_1 | motivation | motivation | intrinsic | intrinsic | extrinsic | extrinsic | need achievement theory | need achievement theory | attribution theory | attribution theoryLicense

Except for third party materials and otherwise stated (see http://www.open.ac.uk/conditions terms and conditions), this content is made available under a http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Licence Licensed under a Creative Commons Attribution - NonCommercial-ShareAlike 2.0 Licence - see http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ - Original copyright The Open UniversitySite sourced from

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In this course we shall develop theoretical methods suitable for the description of the many-body phenomena, such as Hamiltonian second-quantized operator formalism, Greens functions, path integral, functional integral, and the quantum kinetic equation. The concepts to be introduced include, but are not limited to, the random phase approximation, the mean field theory (aka saddle-point, or semiclassical approximation), the tunneling dynamics in imaginary time, instantons, Berry phase, coherent state path integral, renormalization group. In this course we shall develop theoretical methods suitable for the description of the many-body phenomena, such as Hamiltonian second-quantized operator formalism, Greens functions, path integral, functional integral, and the quantum kinetic equation. The concepts to be introduced include, but are not limited to, the random phase approximation, the mean field theory (aka saddle-point, or semiclassical approximation), the tunneling dynamics in imaginary time, instantons, Berry phase, coherent state path integral, renormalization group.Subjects

condensed matter systems | condensed matter systems | low-dimension magnetic and electronic systems | low-dimension magnetic and electronic systems | disorder and quantum transport | disorder and quantum transport | magnetic impurities | magnetic impurities | the Kondo problem | the Kondo problem | quantum spin systems | quantum spin systems | the Hubbard model | the Hubbard model | high temperature superconductors | high temperature superconductors | Bose Condensates | Bose Condensates | Quasiparticles | Quasiparticles | Collective Modes | Collective Modes | Superfluidity | Superfluidity | Vortices | Vortices | Fermi Gases | Fermi Gases | Fermi Liquids | Fermi Liquids | Collective Excitations | Collective Excitations | Cooper Pairing | Cooper Pairing | BCS Theory | BCS Theory | Off-diagonal Long-range Order | Off-diagonal Long-range Order | Superconductivity | Superconductivity | Atom Interacting | Atom Interacting | Optical Fields | Optical Fields | Lamb Shift | Lamb Shift | Casimir Effect | Casimir Effect | Dicke Superradiance | Dicke Superradiance | Quantum Transport | Quantum Transport | Wave Scattering | Wave Scattering | Disordered Media | Disordered Media | Localization | Localization | Tunneling | Tunneling | Instantons | Instantons | Macroscopic Quantum Systems | Macroscopic Quantum Systems | Coupling | Coupling | Thermal Bath | Thermal Bath | Spin-boson Model | Spin-boson Model | Kondo Effect | Kondo Effect | Spin Dynamics | Spin Dynamics | Gases Transport | Gases Transport | Solids Transport | Solids Transport | Cold Atoms | Cold Atoms | Optical Lattices | Optical Lattices | Quantum Theory | Quantum Theory | Photodetection | Photodetection | Electric Noise | Electric NoiseLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata24.910 Topics in Linguistics Theory (MIT) 24.910 Topics in Linguistics Theory (MIT)

Description

I realize that "Modes of Assertion" is a rather cryptic title for the course. What we will explore are ways of modulating the force of an assertion. This will engage us in formal semantics and pragmatics, the theory of speech acts and performative utterances, and quite a bit of empirical work on a not-too-well understood complex of data. "It is obvious that he made a big mistake." If you're like me you didn't feel much of a difference. But now see what happens when you embed the two sentences: "We have to fire him, because he obviously made a big mistake." "We have to fire him, because it is obvious that he made a big mistake." One of the two examples is unremarkable, the other suggests that the reason he needs to be fired is not that he made a big I realize that "Modes of Assertion" is a rather cryptic title for the course. What we will explore are ways of modulating the force of an assertion. This will engage us in formal semantics and pragmatics, the theory of speech acts and performative utterances, and quite a bit of empirical work on a not-too-well understood complex of data. "It is obvious that he made a big mistake." If you're like me you didn't feel much of a difference. But now see what happens when you embed the two sentences: "We have to fire him, because he obviously made a big mistake." "We have to fire him, because it is obvious that he made a big mistake." One of the two examples is unremarkable, the other suggests that the reason he needs to be fired is not that he made a bigSubjects

linguistic theory | linguistic theory | semantics | semantics | typology | typology | preformatics | preformatics | modality | modality | evidentiality | evidentiality | embedded assertions | embedded assertions | modes of assertion | modes of assertion | modulation | modulation | force | force | formal semantics | formal semantics | pragmatics | pragmatics | speech acts | speech acts | performative utterances | performative utterances | language constructions | language constructions | English | English | German | German | Quechua | Quechua | Tibetan | Tibetan | evidentiality marking | evidentiality marking | epistemic modality | epistemic modality | performatives | performatives | evidentials | evidentials | direct evidentiality | direct evidentiality | indirect evidentiality | indirect evidentiality | conditionals | conditionals | Faller?s ideas | Faller?s ideas | best possible grounds | best possible groundsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.079 Introduction to Convex Optimization (MIT) 6.079 Introduction to Convex Optimization (MIT)

Description

This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software. Acknowledgements The course materials were developed jointly by Prof. Stephen Boyd (Stanford This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications. Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software. Acknowledgements The course materials were developed jointly by Prof. Stephen Boyd (StanfordSubjects

convex sets | convex sets | convex functions | convex functions | optimization problems | optimization problems | least-squares | least-squares | linear and quadratic programs | linear and quadratic programs | semidefinite programming | semidefinite programming | optimality conditions | optimality conditions | duality theory | duality theoryLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata15.060 Data, Models, and Decisions (MIT) 15.060 Data, Models, and Decisions (MIT)

Description

This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice. This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice.Subjects

decision analysis | decision analysis | discrete probability distributions | discrete probability distributions | continuous probability distributions | continuous probability distributions | normal probability distribution | normal probability distribution | statistical sampling | statistical sampling | regression models | regression models | linear optimization | linear optimization | nonlinear optimization | nonlinear optimization | discrete optimization | discrete optimizationLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata15.060 Data, Models, and Decisions (MIT) 15.060 Data, Models, and Decisions (MIT)

Description

This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice. This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice.Subjects

decision analysis | decision analysis | discrete probability distributions | discrete probability distributions | continuous probability distributions | continuous probability distributions | normal probability distribution | normal probability distribution | statistical sampling | statistical sampling | regression models | regression models | linear optimization | linear optimization | nonlinear optimization | nonlinear optimization | discrete optimization | discrete optimizationLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata18.443 Statistics for Applications (MIT) 18.443 Statistics for Applications (MIT)

Description

This course provides a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. The course topics include hypothesis testing and estimation. It also includes confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, and correlation. This course provides a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. The course topics include hypothesis testing and estimation. It also includes confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, and correlation.Subjects

hypothesis testing and estimation; confidence intervals; chi-square tests; nonparametric statistics; analysis of variance; regression; correlation | hypothesis testing and estimation; confidence intervals; chi-square tests; nonparametric statistics; analysis of variance; regression; correlation | hypothesis testing and estimation | hypothesis testing and estimation | confidence intervals | confidence intervals | chi-square tests | chi-square tests | nonparametric statistics | nonparametric statistics | analysis of variance | analysis of variance | regression | regression | correlation | correlationLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course introduces undergraduates to the basic theory, institutional architecture, and practice of international development. We take an applied, interdisciplinary approach to some of the "big questions" in our field. This course will unpack these questions by providing an overview of existing knowledge and best practices in the field. The goal of this class is to go beyond traditional dichotomies and narrow definitions of progress, well-being, and culture. Instead, we will invite students to develop a more nuanced understanding of international development by offering an innovative set of tools and content flexibility. This course introduces undergraduates to the basic theory, institutional architecture, and practice of international development. We take an applied, interdisciplinary approach to some of the "big questions" in our field. This course will unpack these questions by providing an overview of existing knowledge and best practices in the field. The goal of this class is to go beyond traditional dichotomies and narrow definitions of progress, well-being, and culture. Instead, we will invite students to develop a more nuanced understanding of international development by offering an innovative set of tools and content flexibility.Subjects

international development | international development | poverty | poverty | development | development | governments | governments | markets | markets | structure | structure | agency | agency | wellbeing | wellbeing | progress | progress | culture | culture | policy | policy | socioeconomic | socioeconomic | colonialism | colonialism | ethical development | ethical development | identities | identities | modernization | modernization | growth paradigms | growth paradigms | development agenda | development agenda | industrialization | industrialization | debt crisis | debt crisis | globalization | globalization | washington consensus | washington consensus | institutions | institutions | continuous development | continuous development | bretton woods system | bretton woods system | cooperation | cooperation | NGOs | NGOs | non-governmental organization | non-governmental organization | capitalism | capitalism | private sector | private sector | development theory | development theory | international aid architecture | international aid architectureLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject. Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.Subjects

supervised learning | supervised learning | statistical learning | statistical learning | multivariate function | multivariate function | Support Vector Machines | Support Vector Machines | regression | regression | classification | classification | VC theory | VC theory | computer vision | computer vision | computer graphics | computer graphics | bioinformatics | bioinformaticsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata15.060 Data, Models, and Decisions (MIT) 15.060 Data, Models, and Decisions (MIT)

Description

This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice. This course is designed to introduce first-year MBA students to the fundamental quantitative techniques of using data to make informed management decisions. In particular, the course focuses on various ways of modeling, or thinking structurally about, decision problems in order to enhance decision-making skills. Topics include decision analysis, probability, random variables, statistical estimation, regression, simulation, linear optimization, as well as nonlinear and discrete optimization. Management cases are used extensively to illustrate the practical use of modeling tools to improve the management practice.Subjects

decision analysis | decision analysis | discrete probability distributions | discrete probability distributions | continuous probability distributions | continuous probability distributions | normal probability distribution | normal probability distribution | statistical sampling | statistical sampling | regression models | regression models | linear optimization | linear optimization | nonlinear optimization | nonlinear optimization | discrete optimization | discrete optimizationLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This class covers a range of topics including hypertext, interactive cinema, games, installation art, and soundscapes. It examines the potential for dynamic narrative in traditional media like novels and films and as well as in computer-based stories and games. The course focuses on the creation of electronic stories and games using simple authoring systems and multimedia software tools. Students present and constructively critique one another's work in progress in a workshop setting aimed at expanding the representational powers of a new creative medium. This class covers a range of topics including hypertext, interactive cinema, games, installation art, and soundscapes. It examines the potential for dynamic narrative in traditional media like novels and films and as well as in computer-based stories and games. The course focuses on the creation of electronic stories and games using simple authoring systems and multimedia software tools. Students present and constructively critique one another's work in progress in a workshop setting aimed at expanding the representational powers of a new creative medium.Subjects

Theory | Theory | Practice | Practice | Non-linear | Non-linear | Interactive | Interactive | Narrative | Narrative | Film | Film | Writing | Writing | Games | Games | Web | Web | HTML | HTML | Multilinear | Multilinear | Story | Story | creative writing | creative writing | computers | computers | book-based narratives | book-based narratives | structure | structure | digression | digression | multiple points of view | multiple points of view | storyline | storyline | hypertexts | hypertexts | adventure games | adventure games | artificial intelligence programs | artificial intelligence programs | eliza | eliza | modeling | modeling | computer-based narratives | computer-based narratives | 21W.765 | 21W.765 | 21L.489 | 21L.489License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.013 Electromagnetics and Applications (MIT) 6.013 Electromagnetics and Applications (MIT)

Description

This course explores electromagnetic phenomena in modern applications, including wireless communications, circuits, computer interconnects and peripherals, optical fiber links and components, microwave communications and radar, antennas, sensors, micro-electromechanical systems, motors, and power generation and transmission. Fundamentals covered include: quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided and unguided waves; resonance; and forces, power, and energy.Acknowledgments The instructors would like to thank Robert Haussman for transcribing into LaTeX the problem set and Quiz 2 solutions. This course explores electromagnetic phenomena in modern applications, including wireless communications, circuits, computer interconnects and peripherals, optical fiber links and components, microwave communications and radar, antennas, sensors, micro-electromechanical systems, motors, and power generation and transmission. Fundamentals covered include: quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided and unguided waves; resonance; and forces, power, and energy.Acknowledgments The instructors would like to thank Robert Haussman for transcribing into LaTeX the problem set and Quiz 2 solutions.Subjects

ESD.013 | ESD.013 | electromagnetics | electromagnetics | applications | applications | wireless communications | wireless communications | circuits | circuits | computer interconnects | computer interconnects | peripherals | peripherals | optical fiber links | optical fiber links | microwave communications | microwave communications | radar | radar | antennas | antennas | sensors | sensors | micro-electromechanical systems | micro-electromechanical systems | power generation | power generation | power transmission | power transmission | quasistatic solutions | quasistatic solutions | dynamic solutions | dynamic solutions | Maxwell | Maxwell | Maxwell's equations | Maxwell's equations | waves | waves | radiation | radiation | diffraction | diffraction | guided waves | guided waves | unguided waves | unguided waves | resonance | resonance | forces | forces | power | power | energy | energyLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment. Additional Facul This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment. Additional FaculSubjects

medical imaging | medical imaging | medical lab | medical lab | medical technology | medical technology | magnetic resonance imaging | magnetic resonance imaging | MRI | MRI | fMRI | fMRI | signal processing | signal processing | human brain mapping | human brain mapping | function | function | image formation physics | image formation physics | metabolism | metabolism | psychology | psychology | physiology | physiology | image signals | image signals | image processing | image processing | parenchymal | parenchymal | cerebrovascular neuroanatomy | cerebrovascular neuroanatomy | neurology | neurology | functional data analysis | functional data analysis | experimental design | experimental design | statistical models | statistical models | human subjects | human subjects | informed consent | informed consent | institutional review board requirements | institutional review board requirements | safety | safety | medical | medical | brain scan | brain scan | brain imaging | brain imaging | DTI | DTI | vision | visionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This practicum subject integrates theory and practice through the design, implementation, and evaluation of a comprehensive community information infrastructure that promotes democratic involvement and informs community development projects. Students work with Lawrence Community Works, Inc. to involve constituents and generate solutions to an important planning problem in the City of Lawrence, Massachusetts. Final project presentations take place in a public forum, and serve to inform future development of the information infrastructure. Subject begins with an overview of the digital divide, e-government, public participation GIS, and neighborhood information systems. Subject includes a reflection component and a deliberate investigation of race, class, and gender dynamics. This practicum subject integrates theory and practice through the design, implementation, and evaluation of a comprehensive community information infrastructure that promotes democratic involvement and informs community development projects. Students work with Lawrence Community Works, Inc. to involve constituents and generate solutions to an important planning problem in the City of Lawrence, Massachusetts. Final project presentations take place in a public forum, and serve to inform future development of the information infrastructure. Subject begins with an overview of the digital divide, e-government, public participation GIS, and neighborhood information systems. Subject includes a reflection component and a deliberate investigation of race, class, and gender dynamics.Subjects

theory and practice | theory and practice | implementation | implementation | evaluation | evaluation | comprehensive community information infrastructure | comprehensive community information infrastructure | democratic involvement | democratic involvement | community development projects | community development projects | Lawrence Community Works | Lawrence Community Works | Inc. | Inc. | planning problem in the City of Lawrence | planning problem in the City of Lawrence | Massachusetts | Massachusetts | the digital divide | the digital divide | e-government | e-government | public participation | public participation | GIS | GIS | neighborhood information systems | neighborhood information systems | Lawrence Community Works | Inc. | Lawrence Community Works | Inc. | planning problem in the City of Lawrence | Massachusetts | planning problem in the City of Lawrence | MassachusettsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection. This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | Input-output | state-space models | state-space models | linear systems | linear systems | deterministic and random signals | deterministic and random signals | time- and transform-domain representations | time- and transform-domain representations | sampling | sampling | discrete-time processing | discrete-time processing | continuous-time signals | continuous-time signals | state feedback | state feedback | observers | observers | probabilistic models | probabilistic models | stochastic processes | stochastic processes | correlation functions | correlation functions | power spectra | power spectra | whitening filters | whitening filters | Detection | Detection | matched filters | matched filters | Least-mean square error estimation | Least-mean square error estimation | Wiener filtering | Wiener filteringLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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