Searching for representation : 346 results found | RSS Feed for this search

1 2 3 4 5 6 7 8 9 10 11 12 13 14

4.107 MArch Portfolio Seminar (MIT) 4.107 MArch Portfolio Seminar (MIT)

Description

The aim of the Portfolio Seminar is to assist in developing a critical position in relationship to their design work. By engaging multiple forms of representation, written and visual, students will explore methods that facilitate describing and representing their design work. Through a critical assessment of their existing portfolios, students will first be challenged to articulate design theses and interests in their past projects. Different mediums of representation will then be studied in order to hone an understanding of the relationship between form and content, and more specifically, the understanding of particular modes of representation as different filters through which their work can be read. Some of the questions that will be addressed are: How does one go about describing an i The aim of the Portfolio Seminar is to assist in developing a critical position in relationship to their design work. By engaging multiple forms of representation, written and visual, students will explore methods that facilitate describing and representing their design work. Through a critical assessment of their existing portfolios, students will first be challenged to articulate design theses and interests in their past projects. Different mediums of representation will then be studied in order to hone an understanding of the relationship between form and content, and more specifically, the understanding of particular modes of representation as different filters through which their work can be read. Some of the questions that will be addressed are: How does one go about describing an iSubjects

representation | representation | portfolio | portfolio | digital | digital | written | written | communicating design | communicating design | meta-level design | meta-level design | theory | theory | representational media | representational media | words vs image | words vs image | physical vs digital | physical vs digital | design vs representation | design vs representation | multiple media | multiple media | architecture and representation | architecture and representation | design thesis | design thesis | web publishing | web publishing | architecture | architecture | description | descriptionLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

This studio explores the notion of in-between by engaging several relationships; the relationship between intervention and perception, between representation and notation and between the fixed and the temporal. In the Exactitude in Science, Jorge Luis Borges tells the perverse tale of the one to one scale map, where the desire for precision and power leads to the escalating production of larger and more accurate maps of the territory. For Jean Baudrillard, "The territory no longer precedes the map nor survives it. …it is the map that precedes the territory... and thus, it would be the territory whose shreds are slowly rotting across the map." The map or the territory, left to ruin-shredding across the 'other', beautifully captures the tension between reality and representati This studio explores the notion of in-between by engaging several relationships; the relationship between intervention and perception, between representation and notation and between the fixed and the temporal. In the Exactitude in Science, Jorge Luis Borges tells the perverse tale of the one to one scale map, where the desire for precision and power leads to the escalating production of larger and more accurate maps of the territory. For Jean Baudrillard, "The territory no longer precedes the map nor survives it. …it is the map that precedes the territory... and thus, it would be the territory whose shreds are slowly rotting across the map." The map or the territory, left to ruin-shredding across the 'other', beautifully captures the tension between reality and representatiSubjects

in-between | in-between | relationships | relationships | intervention and perception | intervention and perception | representation and notation | representation and notation | fixed and temporal | fixed and temporal | Borges | Borges | mapping | mapping | territory | territory | Baudrillard | Baudrillard | the 'other' | the 'other' | reality and representation | reality and representation | collective desire and territorial surface | collective desire and territorial surface | filter | filter | create | create | frame | frame | scale | scale | orient | orient | project | project | agency | agency | landscape | landscape | architecture | architecture | urbanism | urbanism | representation versus real | representation versus real | design | design | perception | perception | representation | representation | fixed | fixed | temporal | temporal | map | map | reality | reality | collective desire | collective desire | territorial surface | territorial surfaceLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata6.003 Signals and Systems (MIT) 6.003 Signals and Systems (MIT)

Description

6.003 covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing. 6.003 covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.Subjects

signal and system analysis | signal and system analysis | representations of discrete-time and continuous-time signals | representations of discrete-time and continuous-time signals | representations of linear time-invariant systems | representations of linear time-invariant systems | Fourier representations | Fourier representations | Laplace and Z transforms | Laplace and Z transforms | sampling | sampling | difference and differential equations | difference and differential equations | feedback and control | feedback and control | communications | communications | signal processing | signal processingLicense

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataReadme file for Introduction to Artificial Intelligence

Description

This readme file contains details of links to all the Introduction to Artificial Intelligence module's material held on Jorum and information about the module as well.Subjects

ukoer | evolutionary algorithm lecture | algorithm tutorial | genetic algorithm lecture | genetic algorithm example | evolutionary computation tutorial | artificial intelligence lecture | artificial intelligence tutorial | random processes reading material | semantic web reading material | neural networks video | evolutionary computation test | artificial intelligence test | knowledge representation test | neural networks test | evolutionary algorithm | genetic computation | genetic programming | evolutionary computation | artificial intelligence | introduction to artificial intelligence | search | problem solving | revision | knowledge representation | semantic web | neural network | neural networks | artificial neural networks | swarm intelligence | collective intelligence | robot societies | genetic computation lecture | genetic programming lecture | evolutionary computation lecture | introduction to artificial intelligence lecture | evolutionary algorithm tutorial | genetic computation tutorial | genetic programming tutorial | introduction to artificial intelligence tutorial | evolutionary algorithm example | genetic computation example | genetic programming example | evolutionary computation example | artificial intelligence example | introduction to artificial intelligence example | search lecture | problem solving lecture | search tutorial | problem solving tutorial | search example | problem solving example | revision reading material | search reading material | artificial intelligence reading material | introduction to artificial intelligence reading material | revision lecture | knowledge representation lecture | semantic web lecture | knowledge representation practical | semantic web practical | artificial intelligence practical | introduction to artificial intelligence practical | knowledge representation reading material | knowledge representation notes | semantic web notes | artificial intelligence notes | introduction to artificial intelligence notes | neural network lecture | neural networks lecture | artificial neural networks lecture | neural network reading material | neural networks reading material | artificial neural networks reading material | neural network practical | neural networks practical | artificial neural networks practical | neural network viewing material | neural networks viewing material | artificial neural networks viewing material | artificial intelligence viewing material | introduction to artificial intelligence viewing material | swarm intelligence lecture | collective intelligence lecture | robot societies lecture | swarm intelligence tutorial | collective intelligence tutorial | robot societies tutorial | evolutionary algorithm test | genetic computation test | genetic programming test | introduction to artificial intelligence test | search test | problem solving test | semantic web test | neural network test | artificial neural networks test | g700 | ai | g700 lecture | ai lecture | g700 tutorial | ai tutorial | g700 example | ai example | g700 reading material | ai reading material | g700 practical | ai practical | g700 notes | ai notes | g700 viewing material | ai viewing material | g700 test | ai test | Computer science | I100License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/Site sourced from

http://dspace.jorum.ac.uk/oai/request?verb=ListRecords&metadataPrefix=oai_dcAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata13.472J Computational Geometry (MIT) 13.472J Computational Geometry (MIT)

Description

Topics in surface modeling: b-splines, non-uniform rational b-splines, physically based deformable surfaces, sweeps and generalized cylinders, offsets, blending and filleting surfaces. Non-linear solvers and intersection problems. Solid modeling: constructive solid geometry, boundary representation, non-manifold and mixed-dimension boundary representation models, octrees. Robustness of geometric computations. Interval methods. Finite and boundary element discretization methods for continuum mechanics problems. Scientific visualization. Variational geometry. Tolerances. Inspection methods. Feature representation and recognition. Shape interrogation for design, analysis, and manufacturing. Involves analytical and programming assignments. Topics in surface modeling: b-splines, non-uniform rational b-splines, physically based deformable surfaces, sweeps and generalized cylinders, offsets, blending and filleting surfaces. Non-linear solvers and intersection problems. Solid modeling: constructive solid geometry, boundary representation, non-manifold and mixed-dimension boundary representation models, octrees. Robustness of geometric computations. Interval methods. Finite and boundary element discretization methods for continuum mechanics problems. Scientific visualization. Variational geometry. Tolerances. Inspection methods. Feature representation and recognition. Shape interrogation for design, analysis, and manufacturing. Involves analytical and programming assignments.Subjects

surface modeling | surface modeling | b-splines | b-splines | deformable surfaces | deformable surfaces | generalized cylinders | generalized cylinders | offsets | offsets | filleting surfaces | filleting surfaces | Non-linear solvers and intersection problems | Non-linear solvers and intersection problems | Solid modeling | Solid modeling | boundary representation | boundary representation | non-manifold and mixed-dimension boundary representation models | non-manifold and mixed-dimension boundary representation models | octrees | octrees | Interval methods | Interval methods | discretization methods | discretization methods | Scientific visualization | Scientific visualization | Variational geometry | Variational geometry | Tolerances | Tolerances | Inspection methods | Inspection methods | Shape interrogation | Shape interrogation | 2.158J | 2.158J | 1.128J | 1.128J | 16.940J | 16.940J | 13.472 | 13.472 | 2.158 | 2.158 | 1.128 | 1.128 | 16.940 | 16.940License

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation. This course provides a broad theoretical basis for system identification, estimation, and learning. Students will study least squares estimation and its convergence properties, Kalman filters, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation.Subjects

system identification; estimation; least squares estimation; Kalman filter; noise dynamics; system representation; function approximation theory; neural nets; radial basis functions; wavelets; volterra expansions; informative data sets; persistent excitation; asymptotic variance; central limit theorem; model structure selection; system order estimate; maximum likelihood; unbiased estimates; Cramer-Rao lower bound; Kullback-Leibler information distance; Akaike?s information criterion; experiment design; model validation. | system identification; estimation; least squares estimation; Kalman filter; noise dynamics; system representation; function approximation theory; neural nets; radial basis functions; wavelets; volterra expansions; informative data sets; persistent excitation; asymptotic variance; central limit theorem; model structure selection; system order estimate; maximum likelihood; unbiased estimates; Cramer-Rao lower bound; Kullback-Leibler information distance; Akaike?s information criterion; experiment design; model validation. | system identification | system identification | estimation | estimation | least squares estimation | least squares estimation | Kalman filter | Kalman filter | noise dynamics | noise dynamics | system representation | system representation | function approximation theory | function approximation theory | neural nets | neural nets | radial basis functions | radial basis functions | wavelets | wavelets | volterra expansions | volterra expansions | informative data sets | informative data sets | persistent excitation | persistent excitation | asymptotic variance | asymptotic variance | central limit theorem | central limit theorem | model structure selection | model structure selection | system order estimate | system order estimate | maximum likelihood | maximum likelihood | unbiased estimates | unbiased estimates | Cramer-Rao lower bound | Cramer-Rao lower bound | Kullback-Leibler information distance | Kullback-Leibler information distance | Akaike?s information criterion | Akaike?s information criterion | experiment design | experiment design | model validation | model validationLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata2.158J Computational Geometry (MIT) 2.158J Computational Geometry (MIT)

Description

Topics in surface modeling: b-splines, non-uniform rational b-splines, physically based deformable surfaces, sweeps and generalized cylinders, offsets, blending and filleting surfaces. Non-linear solvers and intersection problems. Solid modeling: constructive solid geometry, boundary representation, non-manifold and mixed-dimension boundary representation models, octrees. Robustness of geometric computations. Interval methods. Finite and boundary element discretization methods for continuum mechanics problems. Scientific visualization. Variational geometry. Tolerances. Inspection methods. Feature representation and recognition. Shape interrogation for design, analysis, and manufacturing. Involves analytical and programming assignments. This course was originally offered in Course 13 (Depar Topics in surface modeling: b-splines, non-uniform rational b-splines, physically based deformable surfaces, sweeps and generalized cylinders, offsets, blending and filleting surfaces. Non-linear solvers and intersection problems. Solid modeling: constructive solid geometry, boundary representation, non-manifold and mixed-dimension boundary representation models, octrees. Robustness of geometric computations. Interval methods. Finite and boundary element discretization methods for continuum mechanics problems. Scientific visualization. Variational geometry. Tolerances. Inspection methods. Feature representation and recognition. Shape interrogation for design, analysis, and manufacturing. Involves analytical and programming assignments. This course was originally offered in Course 13 (DeparSubjects

surface modeling | surface modeling | b-splines | b-splines | deformable surfaces | deformable surfaces | generalized cylinders | generalized cylinders | offsets | offsets | filleting surfaces | filleting surfaces | Non-linear solvers and intersection problems | Non-linear solvers and intersection problems | Solid modeling | Solid modeling | boundary representation | boundary representation | non-manifold and mixed-dimension boundary representation models | non-manifold and mixed-dimension boundary representation models | octrees | octrees | Interval methods | Interval methods | discretization methods | discretization methods | Scientific visualization | Scientific visualization | Variational geometry | Variational geometry | Tolerances | Tolerances | Inspection methods | Inspection methods | Shape interrogation | Shape interrogation | 13.472J | 13.472J | 13.472 | 13.472 | 2.158 | 2.158 | 1.128 | 1.128 | 16.940 | 16.940License

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

http://ocw.mit.edu/rss/all/mit-allcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others. For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical f Most algorithms in computer vision and image analysis can be understood in terms of two important components: a representation and a modeling/estimation algorithm. The representation defines what information is important about the objects and is used to describe them. The modeling techniques extract the information from images to instantiate the representation for the particular objects present in the scene. In this seminar, we will discuss popular representations (such as contours, level sets, deformation fields) and useful methods that allow us to extract and manipulate image information, including manifold fitting, markov random fields, expectation maximization, clustering and others. For each concept -- a new representation or an estimation algorithm -- a lecture on the mathematical fSubjects

computer vision | computer vision | image analysis | image analysis | representation algorithm | representation algorithm | modeling | modeling | estimation algorithm | estimation algorithm | information | information | objects | objects | modeling techniques | modeling techniques | images | images | representations | representations | contours | contours | level sets | level sets | deformation fields | deformation fields | image information | image information | manifold fitting | manifold fitting | markov random fields | markov random fields | expectation maximization | expectation maximization | clustering | clustering | mathematical foundations | mathematical foundations | medical and biological imaging | medical and biological imagingLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses-6.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

This course takes a 'back to the beginning' view that aims to better understand the end result. What might be the developmental processes that lead to the organization of 'booming, buzzing confusions' into coherent visual objects? This course examines key experimental results and computational proposals pertinent to the discovery of objects in complex visual inputs. The structure of the course is designed to get students to learn and to focus on the genre of study as a whole; to get a feel for how science is done in this field. This course takes a 'back to the beginning' view that aims to better understand the end result. What might be the developmental processes that lead to the organization of 'booming, buzzing confusions' into coherent visual objects? This course examines key experimental results and computational proposals pertinent to the discovery of objects in complex visual inputs. The structure of the course is designed to get students to learn and to focus on the genre of study as a whole; to get a feel for how science is done in this field.Subjects

computational theories of human cognition | computational theories of human cognition | principles of inductive learning and inference | principles of inductive learning and inference | representation of knowledge | representation of knowledge | computational frameworks | computational frameworks | Bayesian models | Bayesian models | hierarchical Bayesian models | hierarchical Bayesian models | probabilistic graphical models | probabilistic graphical models | nonparametric statistical models | nonparametric statistical models | Bayesian Occam's razor | Bayesian Occam's razor | sampling algorithms for approximate learning and inference | sampling algorithms for approximate learning and inference | probabilistic models defined over structured representations such as first-order logic | probabilistic models defined over structured representations such as first-order logic | grammars | grammars | relational schemas | relational schemas | core aspects of cognition | core aspects of cognition | concept learning | concept learning | concept categorization | concept categorization | causal reasoning | causal reasoning | theory formation | theory formation | language acquisition | language acquisition | social inference | social inferenceLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses-9.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning. Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning.Subjects

vision | vision | object recognition | object recognition | monkey versus human | monkey versus human | object representations | object representations | fMRI | fMRI | temporal lobe | temporal lobe | visual cortex | visual cortex | neuronal representations | neuronal representations | neurophysiology | neurophysiology | retinal image | retinal image | pattern recognition | pattern recognition | perceptual awareness | perceptual awarenessLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses-9.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

Wavelets are localized basis functions, good for representing short-time events. The coefficients at each scale are filtered and subsampled to give coefficients at the next scale. This is Mallat's pyramid algorithm for multiresolution, connecting wavelets to filter banks. Wavelets and multiscale algorithms for compression and signal/image processing are developed. Subject is project-based for engineering and scientific applications. Wavelets are localized basis functions, good for representing short-time events. The coefficients at each scale are filtered and subsampled to give coefficients at the next scale. This is Mallat's pyramid algorithm for multiresolution, connecting wavelets to filter banks. Wavelets and multiscale algorithms for compression and signal/image processing are developed. Subject is project-based for engineering and scientific applications.Subjects

Discrete-time filters | Discrete-time filters | convolution | convolution | Fourier transform | Fourier transform | owpass and highpass filters | owpass and highpass filters | Sampling rate change operations | Sampling rate change operations | upsampling and downsampling | upsampling and downsampling | ractional sampling | ractional sampling | interpolation | interpolation | Filter Banks | Filter Banks | time domain (Haar example) and frequency domain | time domain (Haar example) and frequency domain | conditions for alias cancellation and no distortion | conditions for alias cancellation and no distortion | perfect reconstruction | perfect reconstruction | halfband filters and possible factorizations | halfband filters and possible factorizations | Modulation and polyphase representations | Modulation and polyphase representations | Noble identities | Noble identities | block Toeplitz matrices and block z-transforms | block Toeplitz matrices and block z-transforms | polyphase examples | polyphase examples | Matlab wavelet toolbox | Matlab wavelet toolbox | Orthogonal filter banks | Orthogonal filter banks | paraunitary matrices | paraunitary matrices | orthogonality condition (Condition O) in the time domain | orthogonality condition (Condition O) in the time domain | modulation domain and polyphase domain | modulation domain and polyphase domain | Maxflat filters | Maxflat filters | Daubechies and Meyer formulas | Daubechies and Meyer formulas | Spectral factorization | Spectral factorization | Multiresolution Analysis (MRA) | Multiresolution Analysis (MRA) | requirements for MRA | requirements for MRA | nested spaces and complementary spaces; scaling functions and wavelets | nested spaces and complementary spaces; scaling functions and wavelets | Refinement equation | Refinement equation | iterative and recursive solution techniques | iterative and recursive solution techniques | infinite product formula | infinite product formula | filter bank approach for computing scaling functions and wavelets | filter bank approach for computing scaling functions and wavelets | Orthogonal wavelet bases | Orthogonal wavelet bases | connection to orthogonal filters | connection to orthogonal filters | orthogonality in the frequency domain | orthogonality in the frequency domain | Biorthogonal wavelet bases | Biorthogonal wavelet bases | Mallat pyramid algorithm | Mallat pyramid algorithm | Accuracy of wavelet approximations (Condition A) | Accuracy of wavelet approximations (Condition A) | vanishing moments | vanishing moments | polynomial cancellation in filter banks | polynomial cancellation in filter banks | Smoothness of wavelet bases | Smoothness of wavelet bases | convergence of the cascade algorithm (Condition E) | convergence of the cascade algorithm (Condition E) | splines | splines | Bases vs. frames | Bases vs. frames | Signal and image processing | Signal and image processing | finite length signals | finite length signals | boundary filters and boundary wavelets | boundary filters and boundary wavelets | wavelet compression algorithms | wavelet compression algorithms | Lifting | Lifting | ladder structure for filter banks | ladder structure for filter banks | factorization of polyphase matrix into lifting steps | factorization of polyphase matrix into lifting steps | lifting form of refinement equationSec | lifting form of refinement equationSec | Wavelets and subdivision | Wavelets and subdivision | nonuniform grids | nonuniform grids | multiresolution for triangular meshes | multiresolution for triangular meshes | representation and compression of surfaces | representation and compression of surfaces | Numerical solution of PDEs | Numerical solution of PDEs | Galerkin approximation | Galerkin approximation | wavelet integrals (projection coefficients | moments and connection coefficients) | wavelet integrals (projection coefficients | moments and connection coefficients) | convergence | convergence | Subdivision wavelets for integral equations | Subdivision wavelets for integral equations | Compression and convergence estimates | Compression and convergence estimates | M-band wavelets | M-band wavelets | DFT filter banks and cosine modulated filter banks | DFT filter banks and cosine modulated filter banks | Multiwavelets | MultiwaveletsLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

The seminar will be devoted to understanding what we're up to when we ascribe contents to a person's assertions and mental attitudes. We seek to make clear the rules of the game for the philosophy of language. We'll survey classic discussions of the issue by Field, Lewis and Stalnaker. But much of the emphasis of the class will be on getting clear about the limitations of our theoretical tools. I'd like to focus on places where our theorizing runs into trouble, or breaks down altogether. The seminar will be devoted to understanding what we're up to when we ascribe contents to a person's assertions and mental attitudes. We seek to make clear the rules of the game for the philosophy of language. We'll survey classic discussions of the issue by Field, Lewis and Stalnaker. But much of the emphasis of the class will be on getting clear about the limitations of our theoretical tools. I'd like to focus on places where our theorizing runs into trouble, or breaks down altogether.Subjects

radical interpretation | radical interpretation | mathematical truth | mathematical truth | self-location | self-location | degrees of belief | degrees of belief | incoherent belief | incoherent belief | language of thought | language of thought | representation system | representation system | modeling representation | modeling representation | intentionality | intentionality | philosophy of language | philosophy of language | Putnam's paradox | Putnam's paradox | semantics | semantics | logical omniscience | logical omniscience | epistemology | epistemology | knowledge argument | knowledge argumentLicense

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

http://ocw.mit.edu/rss/all/mit-allcourses-24.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataHST.947 Medical Artificial Intelligence (MIT) HST.947 Medical Artificial Intelligence (MIT)

Description

This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers. This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers.Subjects

Introduces representations | techniques | and architectures used to build applied systems | Introduces representations | techniques | and architectures used to build applied systems | computational intelligence | computational intelligence | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | problem-solving paradigms | problem-solving paradigms | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | learning paradigms | learning paradigms | Speculations on the contributions of human vision and language systems to human intelligence | Speculations on the contributions of human vision and language systems to human intelligence | Meets with HST.947 spring only | Meets with HST.947 spring only | 4 Engineering Design Points | 4 Engineering Design Points | artificial intelligence | artificial intelligence | applied systems | applied systems | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systems | diagnosis | diagnosis | clinical simulation | clinical simulation | genomics | genomics | proteomics | proteomics | 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

http://ocw.mit.edu/rss/all/mit-allcourses-HST.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata4.107 MArch Portfolio Seminar (MIT)

Description

The aim of the Portfolio Seminar is to assist in developing a critical position in relationship to their design work. By engaging multiple forms of representation, written and visual, students will explore methods that facilitate describing and representing their design work. Through a critical assessment of their existing portfolios, students will first be challenged to articulate design theses and interests in their past projects. Different mediums of representation will then be studied in order to hone an understanding of the relationship between form and content, and more specifically, the understanding of particular modes of representation as different filters through which their work can be read. Some of the questions that will be addressed are: How does one go about describing an iSubjects

representation | portfolio | digital | written | communicating design | meta-level design | theory | representational media | words vs image | physical vs digital | design vs representation | multiple media | architecture and representation | design thesis | web publishing | architecture | descriptionLicense

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

https://ocw.mit.edu/rss/all/mit-allcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata4.123 Architectural Design, Level I: Perceptions and Processes (MIT)

Description

This studio explores the notion of in-between by engaging several relationships; the relationship between intervention and perception, between representation and notation and between the fixed and the temporal. In the Exactitude in Science, Jorge Luis Borges tells the perverse tale of the one to one scale map, where the desire for precision and power leads to the escalating production of larger and more accurate maps of the territory. For Jean Baudrillard, "The territory no longer precedes the map nor survives it. …it is the map that precedes the territory... and thus, it would be the territory whose shreds are slowly rotting across the map." The map or the territory, left to ruin-shredding across the 'other', beautifully captures the tension between reality and representatiSubjects

in-between | relationships | intervention and perception | representation and notation | fixed and temporal | Borges | mapping | territory | Baudrillard | the 'other' | reality and representation | collective desire and territorial surface | filter | create | frame | scale | orient | project | agency | landscape | architecture | urbanism | representation versus real | design | perception | representation | fixed | temporal | map | reality | collective desire | territorial surfaceLicense

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

https://ocw.mit.edu/rss/all/mit-allcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata6.003 Signals and Systems (MIT)

Description

6.003 covers the fundamentals of signal and system analysis, focusing on representations of discrete-time and continuous-time signals (singularity functions, complex exponentials and geometrics, Fourier representations, Laplace and Z transforms, sampling) and representations of linear, time-invariant systems (difference and differential equations, block diagrams, system functions, poles and zeros, convolution, impulse and step responses, frequency responses). Applications are drawn broadly from engineering and physics, including feedback and control, communications, and signal processing.Subjects

signal and system analysis | representations of discrete-time and continuous-time signals | representations of linear time-invariant systems | Fourier representations | Laplace and Z transforms | sampling | difference and differential equations | feedback and control | communications | signal processingLicense

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

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataComputer Structure Computer Structure

Description

This course describes the basic behavior of a computer, and the main components of a typical computer. This course describes the basic behavior of a computer, and the main components of a typical computer.Subjects

Memory Hierarchy | Memory Hierarchy | Assembly programming | Assembly programming | Introduction to computers | Introduction to computers | Data representation | Data representation | Arquitectura y Tecnologia de Computadores | Arquitectura y Tecnologia de Computadores | ía Informática | ía Informática | 2011 | 2011 | Processor | Processor | Input/Output systems | Input/Output systemsLicense

Copyright 2015, UC3M http://creativecommons.org/licenses/by-nc-sa/4.0/Site sourced from

http://ocw.uc3m.es/ocwuniversia/rss_allAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDigital Electronics Digital Electronics

Description

The basic goal of this subject is to cover an introduction to digital electronics from an updated point of view. To this purpose, the course starts by the fundamentals, i.e., number systems, binary coding and Boolean Algebra. The study of digital circuits begins by logic gates, combinational circuits, including the simplest arithmetic circuits, and ends by sequential circuits, including latches and flip-flops, synchronous sequential circuits, registers and counters. There is one chapter devoted to memories and another one devoted to programmable logic devices. An introduction to digital systems and microprocessors is also included. The basic goal of this subject is to cover an introduction to digital electronics from an updated point of view. To this purpose, the course starts by the fundamentals, i.e., number systems, binary coding and Boolean Algebra. The study of digital circuits begins by logic gates, combinational circuits, including the simplest arithmetic circuits, and ends by sequential circuits, including latches and flip-flops, synchronous sequential circuits, registers and counters. There is one chapter devoted to memories and another one devoted to programmable logic devices. An introduction to digital systems and microprocessors is also included.Subjects

Boolean Algebra | Boolean Algebra | ía de Sistemas Audiovisuales | ía de Sistemas Audiovisuales | Memories | Memories | ática | ática | ía Eléctrica | ía Eléctrica | ía en Tecnologías Industriales | ía en Tecnologías Industriales | Combinational circuits | Combinational circuits | ía Telemática | ía Telemática | Registers and counters | Registers and counters | Digital systems and microprocessors | Digital systems and microprocessors | Synchronous sequential circuits | Synchronous sequential circuits | 2011 | 2011 | ía Electrónica Industrial y Automática | ía Electrónica Industrial y Automática | ía en Tecnologías de Telecomunicación | ía en Tecnologías de Telecomunicación | ía de Sistemas de Comunicaciones | ía de Sistemas de Comunicaciones | Information representation | Information representation | logic gates | logic gates | Arithmetic combinational circuits | Arithmetic combinational circuits | Latches and Flip-flops | Latches and Flip-flops | Tecnologia Electronica | Tecnologia Electronica | Programmable Logic Devices (PLD) | Programmable Logic Devices (PLD)License

Copyright 2015, UC3M http://creativecommons.org/licenses/by-nc-sa/4.0/Site sourced from

http://ocw.uc3m.es/ocwuniversia/rss_allAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata2.29 Numerical Fluid Mechanics (MIT) 2.29 Numerical Fluid Mechanics (MIT)

Description

This course introduces students to MATLAB®. Numerical methods include number representation and errors, interpolation, differentiation, integration, systems of linear equations, and Fourier interpolation and transforms. Students will study partial and ordinary differential equations as well as elliptic and parabolic differential equations, and solutions by numerical integration, finite difference methods, finite element methods, boundary element methods, and panel methods. This course introduces students to MATLAB®. Numerical methods include number representation and errors, interpolation, differentiation, integration, systems of linear equations, and Fourier interpolation and transforms. Students will study partial and ordinary differential equations as well as elliptic and parabolic differential equations, and solutions by numerical integration, finite difference methods, finite element methods, boundary element methods, and panel methods.Subjects

numerical methods | numerical methods | interpolation | interpolation | integration | integration | systems of linear equations | systems of linear equations | differential equations | differential equations | numerical integration | numerical integration | partial differential equations of inviscid hydrodynamics | partial differential equations of inviscid hydrodynamics | finite difference methods | finite difference methods | boundary integral equation panel methods | boundary integral equation panel methods | numerical lifting surface computations | numerical lifting surface computations | Fast Fourier Transforms | Fast Fourier Transforms | Numerical representation | Numerical representation | deterministic and random sea waves | deterministic and random sea waves | Integral boundary layer equations | Integral boundary layer equations | numerical solutions | numerical solutionsLicense

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

This is a new course, whose goal is to give an undergraduate-level introduction to representation theory (of groups, Lie algebras, and associative algebras). Representation theory is an area of mathematics which, roughly speaking, studies symmetry in linear spaces. This is a new course, whose goal is to give an undergraduate-level introduction to representation theory (of groups, Lie algebras, and associative algebras). Representation theory is an area of mathematics which, roughly speaking, studies symmetry in linear spaces.Subjects

finite dimensional algebras | finite dimensional algebras | Quiver Representations | Quiver Representations | series Representations | series Representations | finite groups | finite groups | representation theory | representation theory | Lie algebras | Lie algebras | Tensor products | Tensor products | density theorem | density theorem | Jordan-H?older theorem | Jordan-H?older theorem | Krull-Schmidt theorem | Krull-Schmidt theorem | Maschke?s Theorem | Maschke?s Theorem | Frobenius-Schur indicator | Frobenius-Schur indicator | Frobenius divisibility | Frobenius divisibility | Burnside?s Theorem | Burnside?s TheoremLicense

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata6.034 Artificial Intelligence (MIT) 6.034 Artificial Intelligence (MIT)

Description

6.034 is the header course for the department's "Artificial Intelligence and Applications" concentration. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective. 6.034 is the header course for the department's "Artificial Intelligence and Applications" concentration. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Subjects

artificial intelligence | artificial intelligence | applied systems | applied systems | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systemsLicense

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

This course is offered to undergraduates and introduces basic mathematical models of computation and the finite representation of infinite objects. The course is slower paced than 6.840J/18.404J. Topics covered include: finite automata and regular languages, context-free languages, Turing machines, partial recursive functions, Church's Thesis, undecidability, reducibility and completeness, time complexity and NP-completeness, probabilistic computation, and interactive proof systems. This course is offered to undergraduates and introduces basic mathematical models of computation and the finite representation of infinite objects. The course is slower paced than 6.840J/18.404J. Topics covered include: finite automata and regular languages, context-free languages, Turing machines, partial recursive functions, Church's Thesis, undecidability, reducibility and completeness, time complexity and NP-completeness, probabilistic computation, and interactive proof systems.Subjects

automata | automata | computability | computability | complexity | complexity | mathematical models | mathematical models | computation | computation | finite representation | finite representation | infinite objects | infinite objects | finite automata | finite automata | regular languages | regular languages | context-free languages | context-free languages | Turing machines | Turing machines | partial recursive functions | partial recursive functions | Church's Thesis | Church's Thesis | undecidability | undecidability | reducibility | reducibility | completeness | completeness | time complexity | time complexity | NP-completeness | NP-completeness | probabilistic computation | probabilistic computation | interactive proof systems | interactive proof systems | 6.045 | 6.045 | 18.400 | 18.400License

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

24.901 is designed to give you a preliminary understanding of how the sound systems of different languages are structured, how and why they may differ from each other. The course also aims to provide you with analytical tools in phonology, enough to allow you to sketch the analysis of an entire phonological system by the end of the term. On a non-linguistic level, the couse aims to teach you by example the virtues of formulating precise and explicit descriptive statements; and to develop your skills in making and evaluating arguments. 24.901 is designed to give you a preliminary understanding of how the sound systems of different languages are structured, how and why they may differ from each other. The course also aims to provide you with analytical tools in phonology, enough to allow you to sketch the analysis of an entire phonological system by the end of the term. On a non-linguistic level, the couse aims to teach you by example the virtues of formulating precise and explicit descriptive statements; and to develop your skills in making and evaluating arguments.Subjects

fundamental concepts | fundamental concepts | phonological theory | phonological theory | philosophy | philosophy | cognitive psychology | cognitive psychology | articulatory phonetics | articulatory phonetics | acoustic phonetics | acoustic phonetics | feature systems | feature systems | underlying representations | underlying representations | underspecification | underspecification | phonological rules | phonological rules | phonological derivations | phonological derivations | syllable structure | syllable structure | accentual systems | accentual systems | morphology-phonology interface | morphology-phonology interfaceLicense

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadataDescription

This course offers an introduction to Women's and Gender Studies, an interdisciplinary academic field that asks critical questions about the meaning of gender in society. The primary goal of this course is to familiarize students with key issues, questions and debates in Women's and Gender Studies scholarship, both historical and contemporary. Gender scholarship critically analyzes themes of gendered performance and power in a range of social spheres, such as law, culture, work, medicine and the family. This course offers an introduction to Women's and Gender Studies, an interdisciplinary academic field that asks critical questions about the meaning of gender in society. The primary goal of this course is to familiarize students with key issues, questions and debates in Women's and Gender Studies scholarship, both historical and contemporary. Gender scholarship critically analyzes themes of gendered performance and power in a range of social spheres, such as law, culture, work, medicine and the family.Subjects

women's studies | women's studies | gender | gender | transsexual | transsexual | women's movement | women's movement | women's rights | women's rights | declaration of independence | declaration of independence | madness | madness | illness | illness | patriarchy | patriarchy | female pathology | female pathology | socialization | socialization | ethnicity | ethnicity | race | race | gender roles | gender roles | social construction | social construction | biological essentialism | biological essentialism | embodiment | embodiment | body image | body image | representation of women | representation of women | sexuality | sexuality | reproductive politics | reproductive politics | work | work | violence | violence | feminism | feminismLicense

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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata14.123 Microeconomic Theory III (MIT) 14.123 Microeconomic Theory III (MIT)

Description

This half-semester course discusses decision theory and topics in game theory. We present models of individual decision-making under certainty and uncertainty. Topics include preference orderings, expected utility, risk, stochastic dominance, supermodularity, monotone comparative statics, background risk, game theory, rationalizability, iterated strict dominance multi-stage games, sequential equilibrium, trembling-hand perfection, stability, signaling games, theory of auctions, global games, repeated games, and correlation. This half-semester course discusses decision theory and topics in game theory. We present models of individual decision-making under certainty and uncertainty. Topics include preference orderings, expected utility, risk, stochastic dominance, supermodularity, monotone comparative statics, background risk, game theory, rationalizability, iterated strict dominance multi-stage games, sequential equilibrium, trembling-hand perfection, stability, signaling games, theory of auctions, global games, repeated games, and correlation.Subjects

microeconomics | microeconomics | microeconomic theory | microeconomic theory | preference | preference | utility representation | utility representation | expected utility | expected utility | positive interpretation | positive interpretation | normative interpretation | normative interpretation | risk | risk | stochastic dominance | stochastic dominance | insurance | insurance | finance | finance | supermodularity | supermodularity | comparative statics | comparative statics | decision theory | decision theory | game theory | game theory | rationalizability | rationalizability | iterated strict dominance | iterated strict dominance | iterated conditional dominance | iterated conditional dominance | bargaining | bargaining | equilibrium | equilibrium | sequential equilibrium | sequential equilibrium | trembling-hand perfection | trembling-hand perfection | signaling games | signaling games | auctions | auctions | global games | global games | repeated games | repeated games | 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

http://ocw.mit.edu/rss/all/mit-allarchivedcourses.xmlAttribution

Click to get HTML | Click to get attribution | Click to get URLAll metadata

See all metadata