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6.096 Algorithms for Computational Biology (MIT) 6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks. This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.Subjects

biological sequence analysis | biological sequence analysis | gene finding | gene finding | motif discovery | motif discovery | RNA folding | RNA folding | global and local sequence alignment | global and local sequence alignment | genome assembly | genome assembly | comparative genomics | comparative genomics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | gene expression | gene expression | clustering algorithms | clustering algorithms | scale-free networks | scale-free networks | machine learning applications | machine learning applicationsLicense

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

Description

This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms. This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.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 PointsLicense

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See all metadata6.092 Bioinformatics and Proteomics (MIT) 6.092 Bioinformatics and Proteomics (MIT)

Description

This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon. This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.Subjects

bioinformatics | bioinformatics | proteomics | proteomics | sequence analysis | sequence analysis | microarray expression analysis | microarray expression analysis | Bayesian methods | Bayesian methods | control theory | control theory | scale-free networks | scale-free networks | biotechnology applications | biotechnology applications | real-world examples | real-world examples | actual implementations | actual implementations | engineering design issues | engineering design issues | signal processing | signal processing | network theory | network theory | machine learning | machine learning | robotics | roboticsLicense

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See all metadata6.829 Computer Networks (MIT) 6.829 Computer Networks (MIT)

Description

How does the global network infrastructure work and what are the design principles on which it is based? In what ways are these design principles compromised in practice? How do we make it work better in today's world? How do we ensure that it will work well in the future in the face of rapidly growing scale and heterogeneity? And how should Internet applications be written, so they can obtain the best possible performance both for themselves and for others using the infrastructure? These are some issues that are grappled with in this course. The course will focus on the design, implementation, analysis, and evaluation of large-scale networked systems. Topics include internetworking philosophies, unicast and multicast routing, congestion control, network quality of service, mobile n How does the global network infrastructure work and what are the design principles on which it is based? In what ways are these design principles compromised in practice? How do we make it work better in today's world? How do we ensure that it will work well in the future in the face of rapidly growing scale and heterogeneity? And how should Internet applications be written, so they can obtain the best possible performance both for themselves and for others using the infrastructure? These are some issues that are grappled with in this course. The course will focus on the design, implementation, analysis, and evaluation of large-scale networked systems. Topics include internetworking philosophies, unicast and multicast routing, congestion control, network quality of service, mobile nSubjects

computer | computer | network | network | internetworking | internetworking | unicast | unicast | multicast | multicast | routing | routing | congestion control | congestion control | quality of service | quality of service | mobile networking | mobile networking | router architectures | router architectures | network-aware applications | network-aware applications | content dissemination systems | content dissemination systems | network security | network securityLicense

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See all metadata6.252J Nonlinear Programming (MIT) 6.252J Nonlinear Programming (MIT)

Description

6.252J is a course in the department's "Communication, Control, and Signal Processing" concentration. This course provides a unified analytical and computational approach to nonlinear optimization problems. The topics covered in this course include: unconstrained optimization methods, constrained optimization methods, convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. There is also a comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Throughout the course, applications are drawn from control, communications, power systems, and resource allocation problems. 6.252J is a course in the department's "Communication, Control, and Signal Processing" concentration. This course provides a unified analytical and computational approach to nonlinear optimization problems. The topics covered in this course include: unconstrained optimization methods, constrained optimization methods, convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. There is also a comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Throughout the course, applications are drawn from control, communications, power systems, and resource allocation problems.Subjects

nonlinear programming | nonlinear programming | non-linear programming | non-linear programming | nonlinear optimization | nonlinear optimization | unconstrained optimization | unconstrained optimization | gradient | gradient | conjugate direction | conjugate direction | Newton | Newton | quasi-Newton methods | quasi-Newton methods | constrained optimization | constrained optimization | feasible directions | feasible directions | projection | projection | interior point | interior point | Lagrange multiplier | Lagrange multiplier | convex analysis | convex analysis | Lagrangian relaxation | Lagrangian relaxation | nondifferentiable optimization | nondifferentiable optimization | integer programming | integer programming | optimality conditions | optimality conditions | Lagrange multiplier theory | Lagrange multiplier theory | duality theory | duality theory | control | control | communications | communications | power systems | power systems | resource allocation | resource allocation | 6.252 | 6.252 | 15.084 | 15.084License

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See all metadata8.322 Quantum Theory II (MIT) 8.322 Quantum Theory II (MIT)

Description

8.322 is the second semester of a two-semester subject on quantum theory, stressing principles. Topics covered include: time-dependent perturbation theory and applications to radiation, quantization of EM radiation field, adiabatic theorem and Berry's phase, symmetries in QM, many-particle systems, scattering theory, relativistic quantum mechanics, and Dirac equation. 8.322 is the second semester of a two-semester subject on quantum theory, stressing principles. Topics covered include: time-dependent perturbation theory and applications to radiation, quantization of EM radiation field, adiabatic theorem and Berry's phase, symmetries in QM, many-particle systems, scattering theory, relativistic quantum mechanics, and Dirac equation.Subjects

uncertainty relation | uncertainty relation | observables | observables | eigenstates | eigenstates | eigenvalues | eigenvalues | probabilities of the results of measurement | probabilities of the results of measurement | transformation theory | transformation theory | equations of motion | equations of motion | constants of motion | constants of motion | Symmetry in quantum mechanics | Symmetry in quantum mechanics | representations of symmetry groups | representations of symmetry groups | Variational and perturbation approximations | Variational and perturbation approximations | Systems of identical particles and applications | Systems of identical particles and applications | Time-dependent perturbation theory | Time-dependent perturbation theory | Scattering theory: phase shifts | Scattering theory: phase shifts | Born approximation | Born approximation | The quantum theory of radiation | The quantum theory of radiation | Second quantization and many-body theory | Second quantization and many-body theory | Relativistic quantum mechanics of one electron | Relativistic quantum mechanics of one electron | probability | probability | measurement | measurement | motion equations | motion equations | motion constants | motion constants | symmetry groups | symmetry groups | quantum mechanics | quantum mechanics | variational approximations | variational approximations | perturbation approximations | perturbation approximations | identical particles | identical particles | time-dependent perturbation theory | time-dependent perturbation theory | scattering theory | scattering theory | phase shifts | phase shifts | quantum theory of radiation | quantum theory of radiation | second quantization | second quantization | many-body theory | many-body theory | relativistic quantum mechanics | relativistic quantum mechanics | one electron | one electron | quantization | quantization | EM radiation field | EM radiation field | electromagnetic radiation field | electromagnetic radiation field | adiabatic theorem | adiabatic theorem | Berry?s phase | Berry?s phase | many-particle systems | many-particle systems | Dirac equation | Dirac equation | Hilbert spaces | Hilbert spaces | time evolution | time evolution | Schrodinger picture | Schrodinger picture | Heisenberg picture | Heisenberg picture | interaction picture | interaction picture | classical mechanics | classical mechanics | path integrals | path integrals | EM fields | EM fields | electromagnetic fields | electromagnetic fields | angular momentum | angular momentum | density operators | density operators | quantum measurement | quantum measurement | quantum statistics | quantum statistics | quantum dynamics | quantum dynamicsLicense

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This course is for upper-level graduate students who are planning careers in computational neuroscience. This course 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. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses This course is for upper-level graduate students who are planning careers in computational neuroscience. This course 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. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical usesSubjects

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

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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

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This class covers molecular-level engineering and analysis of chemical processes. The use of chemical bonding, reactivity, and other key concepts in the design and tailoring of organic systems are discussed in this class. Specific class topics include application and development of structure-property relationships, and descriptions of the chemical forces and structural factors that govern supramolecular and interfacial phenomena for molecular and polymeric systems. This class covers molecular-level engineering and analysis of chemical processes. The use of chemical bonding, reactivity, and other key concepts in the design and tailoring of organic systems are discussed in this class. Specific class topics include application and development of structure-property relationships, and descriptions of the chemical forces and structural factors that govern supramolecular and interfacial phenomena for molecular and polymeric systems.Subjects

molecular-level engineering | molecular-level engineering | analysis of chemical processes | analysis of chemical processes | chemical bonding | chemical bonding | reactivity | reactivity | design of organic systems | design of organic systems | tailoring of organic systems | tailoring of organic systems | application and development of structure-property relationships | application and development of structure-property relationships | descriptions of the chemical forces and structural factors that govern supramolecular and interfacial phenomena for molecular and polymeric systems | descriptions of the chemical forces and structural factors that govern supramolecular and interfacial phenomena for molecular and polymeric systemsLicense

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See all metadata10.40 Chemical Engineering Thermodynamics (MIT) 10.40 Chemical Engineering Thermodynamics (MIT)

Description

This course aims to connect the principles, concepts, and laws/postulates of classical and statistical thermodynamics to applications that require quantitative knowledge of thermodynamic properties from a macroscopic to a molecular level. It covers their basic postulates of classical thermodynamics and their application to transient open and closed systems, criteria of stability and equilibria, as well as constitutive property models of pure materials and mixtures emphasizing molecular-level effects using the formalism of statistical mechanics. Phase and chemical equilibria of multicomponent systems are covered. Applications are emphasized through extensive problem work relating to practical cases. This course aims to connect the principles, concepts, and laws/postulates of classical and statistical thermodynamics to applications that require quantitative knowledge of thermodynamic properties from a macroscopic to a molecular level. It covers their basic postulates of classical thermodynamics and their application to transient open and closed systems, criteria of stability and equilibria, as well as constitutive property models of pure materials and mixtures emphasizing molecular-level effects using the formalism of statistical mechanics. Phase and chemical equilibria of multicomponent systems are covered. Applications are emphasized through extensive problem work relating to practical cases.Subjects

thermodynamics | thermodynamics | first law | first law | second law | second law | entropy | entropy | Carnot | Carnot | Gibbs | Gibbs | energy | energy | free energy | free energy | equilibrium | equilibrium | ideal gas | ideal gas | statistical mechanics | statistical mechanics | ensemble | ensemble | Hamiltonian | Hamiltonian | fugacity | fugacity | fluids | fluids | phase | phase | stability | stabilityLicense

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See all metadata12.S990 Quantifying Uncertainty (MIT) 12.S990 Quantifying Uncertainty (MIT)

Description

The ability to quantify the uncertainty in our models of nature is fundamental to many inference problems in Science and Engineering. In this course, we study advanced methods to represent, sample, update and propagate uncertainty. This is a "hands on" course: Methodology will be coupled with applications. The course will include lectures, invited talks, discussions, reviews and projects and will meet once a week to discuss a method and its applications. The ability to quantify the uncertainty in our models of nature is fundamental to many inference problems in Science and Engineering. In this course, we study advanced methods to represent, sample, update and propagate uncertainty. This is a "hands on" course: Methodology will be coupled with applications. The course will include lectures, invited talks, discussions, reviews and projects and will meet once a week to discuss a method and its applications.Subjects

boundary value problems | boundary value problems | Polynomial Chaos | Polynomial Chaos | Hierarchical Bayes | Hierarchical Bayes | Variational Bayes | Variational Bayes | Smoothers | Smoothers | Dimensionality Reduction | Dimensionality Reduction | Sparse Optimization | Sparse OptimizationLicense

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See all metadata12.S56 GPS: Where Are You? (MIT) 12.S56 GPS: Where Are You? (MIT)

Description

This is a freshman advising seminar. The professor of a FAS is the first year advisor to the (no more than 8) students in the seminar. The use of Global Positioning System (GPS) in a wide variety of applications has exploded in the last few years. In this seminar we explore how positions on the Earth were determined before GPS; how GPS itself works and the range of applications in which GPS is now a critical element. This seminar is followed by a UROP research project in the spring semester where results from precise GPS measurements will be analyzed and displayed on the Web. This is a freshman advising seminar. The professor of a FAS is the first year advisor to the (no more than 8) students in the seminar. The use of Global Positioning System (GPS) in a wide variety of applications has exploded in the last few years. In this seminar we explore how positions on the Earth were determined before GPS; how GPS itself works and the range of applications in which GPS is now a critical element. This seminar is followed by a UROP research project in the spring semester where results from precise GPS measurements will be analyzed and displayed on the Web.Subjects

GPS | GPS | global positioning system | global positioning system | navigation | navigation | meteorology | meteorology | geophysics | geophysics | military | militaryLicense

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See all metadata12.215 Modern Navigation (MIT) 12.215 Modern Navigation (MIT)

Description

This course introduces the concepts and applications of navigation techniques using celestial bodies and satellite positioning systems such as the Global Positioning System (GPS). Topics include astronomical observations, radio navigation systems, the relationship between conventional navigation results and those obtained from GPS, and the effects of the security systems, Selective Availability, and anti-spoofing on GPS results. Laboratory sessions cover the use of sextants, astronomical telescopes, and field use of GPS. Application areas covered include ship, automobile, and aircraft navigation and positioning, including very precise positioning applications. This course introduces the concepts and applications of navigation techniques using celestial bodies and satellite positioning systems such as the Global Positioning System (GPS). Topics include astronomical observations, radio navigation systems, the relationship between conventional navigation results and those obtained from GPS, and the effects of the security systems, Selective Availability, and anti-spoofing on GPS results. Laboratory sessions cover the use of sextants, astronomical telescopes, and field use of GPS. Application areas covered include ship, automobile, and aircraft navigation and positioning, including very precise positioning applications.Subjects

Global Positioning System | Global Positioning System | GPScivilian restricted accuracy requirement | GPScivilian restricted accuracy requirement | basic principles | basic principles | science | science | mathematics | mathematicsLicense

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See all metadata14.12 Economic Applications of Game Theory (MIT) 14.12 Economic Applications of Game Theory (MIT)

Description

Game Theory, also known as Multiperson Decision Theory, is the analysis of situations in which the payoff of a decision maker depends not only on his own actions but also on those of others. Game Theory has applications in several fiÂ…elds, such as economics, politics, law, biology, and computer science. In this course, I will introduce the basic tools of game theoretic analysis. In the process, I will outline some of the many applications of Game Theory, primarily in economics. Game Theory, also known as Multiperson Decision Theory, is the analysis of situations in which the payoff of a decision maker depends not only on his own actions but also on those of others. Game Theory has applications in several fiÂ…elds, such as economics, politics, law, biology, and computer science. In this course, I will introduce the basic tools of game theoretic analysis. In the process, I will outline some of the many applications of Game Theory, primarily in economics.Subjects

game theory | game theory | economics | economics | multiperson decision theory | multiperson decision theory | payoff | payoff | games | games | backward induction | backward induction | subgame perfection | subgame perfection | implicit cartels | implicit cartels | dynamic games | dynamic gamesLicense

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See all metadata14.121 Microeconomic Theory I (MIT) 14.121 Microeconomic Theory I (MIT)

Description

This half-semester course provides an introduction to microeconomic theory designed to meet the needs of students in the economics Ph.D. program. Some parts of the course are designed to teach material that all graduate students should know. Others are used to introduce methodologies. Topics include consumer and producer theory, markets and competition, general equilibrium, and tools of comparative statics and their application to price theory. Some topics of recent interest may also be covered. This half-semester course provides an introduction to microeconomic theory designed to meet the needs of students in the economics Ph.D. program. Some parts of the course are designed to teach material that all graduate students should know. Others are used to introduce methodologies. Topics include consumer and producer theory, markets and competition, general equilibrium, and tools of comparative statics and their application to price theory. Some topics of recent interest may also be covered.Subjects

microeconomic theory | microeconomic theory | demand theory | demand theory | producer theory; partial equilibrium | producer theory; partial equilibrium | competitive markets | competitive markets | general equilibrium | general equilibrium | externalities | externalities | Afriat's theorem | Afriat's theorem | pricing | pricing | robust comparative statics | robust comparative statics | utility theory | utility theory | properties of preferences | properties of preferences | choice as primitive | choice as primitive | revealed preference | revealed preference | classical demand theory | classical demand theory | Kuhn-Tucker necessary conditions | Kuhn-Tucker necessary conditions | implications of Walras?s law | implications of Walras?s law | indirect utility functions | indirect utility functions | theorem of the maximum (Berge?s theorem) | theorem of the maximum (Berge?s theorem) | expenditure minimization problem | expenditure minimization problem | Hicksian demands | Hicksian demands | compensated law of demand | compensated law of demand | Slutsky substitution | Slutsky substitution | price changes and welfare | price changes and welfare | compensating variation | compensating variation | and welfare from new goods | and welfare from new goods | price indexes | price indexes | bias in the U.S. consumer price index | bias in the U.S. consumer price index | integrability | integrability | demand aggregation | demand aggregation | aggregate demand and welfare | aggregate demand and welfare | Frisch demands | Frisch demands | and demand estimation | and demand estimation | increasing differences | increasing differences | producer theory applications | producer theory applications | the LeCh?telier principle | the LeCh?telier principle | Topkis? theorem | Topkis? theorem | Milgrom-Shannon monotonicity theorem | Milgrom-Shannon monotonicity theorem | monopoly pricing | monopoly pricing | monopoly and product quality | monopoly and product quality | nonlinear pricing | nonlinear pricing | and price discrimination | and price discrimination | simple models of externalities | simple models of externalities | government intervention | government intervention | Coase theorem | Coase theorem | Myerson-Sattherthwaite proposition | Myerson-Sattherthwaite proposition | missing markets | missing markets | price vs. quantity regulations | price vs. quantity regulations | Weitzman?s analysis | Weitzman?s analysis | uncertainty | uncertainty | common property externalities | common property externalities | optimization | optimization | equilibrium number of boats | equilibrium number of boats | welfare theorems | welfare theorems | uniqueness and determinacy | uniqueness and determinacy | price-taking assumption | price-taking assumption | Edgeworth box | Edgeworth box | welfare properties | welfare properties | Pareto efficiency | Pareto efficiency | Walrasian equilibrium with transfers | Walrasian equilibrium with transfers | Arrow-Debreu economy | Arrow-Debreu economy | separating hyperplanes | separating hyperplanes | Minkowski?s theorem | Minkowski?s theorem | Existence of Walrasian equilibrium | Existence of Walrasian equilibrium | Kakutani?s fixed point theorem | Kakutani?s fixed point theorem | Debreu-Gale-Kuhn-Nikaido lemma | Debreu-Gale-Kuhn-Nikaido lemma | additional properties of general equilibrium | additional properties of general equilibrium | Microfoundations | Microfoundations | core | core | core convergence | core convergence | general equilibrium with time and uncertainty | general equilibrium with time and uncertainty | Jensen?s inequality | Jensen?s inequality | and security market economy | and security market economy | arbitrage pricing theory | arbitrage pricing theory | and risk-neutral probabilities | and risk-neutral probabilities | Housing markets | Housing markets | competitive equilibrium | competitive equilibrium | one-sided matching house allocation problem | one-sided matching house allocation problem | serial dictatorship | serial dictatorship | two-sided matching | two-sided matching | marriage markets | marriage markets | existence of stable matchings | existence of stable matchings | incentives | incentives | housing markets core mechanism | housing markets core mechanismLicense

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See all metadata14.386 New Econometric Methods (MIT) 14.386 New Econometric Methods (MIT)

Description

This course focuses on recent developments in econometrics, especially structural estimation. The topics include nonseparable models, models of imperfect competition, auction models, duration models, and nonlinear panel data. Results are illustrated with economic applications. This course focuses on recent developments in econometrics, especially structural estimation. The topics include nonseparable models, models of imperfect competition, auction models, duration models, and nonlinear panel data. Results are illustrated with economic applications.Subjects

econometrics | econometrics | recent developments | recent developments | structural estimation | structural estimation | nonseparable models | nonseparable models | models of imperfect competition | models of imperfect competition | auction models | auction models | duration models | duration models | and nonlinear panel data | and nonlinear panel data | economic applications | economic applicationsLicense

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See all metadata14.54 International Trade (MIT) 14.54 International Trade (MIT)

Description

This course is an introduction to the theory of international trade and finance with applications to current policy issues. In this course we will cover the basic tools to understand what determines the flow of goods across countries, i.e. international trade, and what determines the flow of savings and investments from one country to another, i.e. international finance. We will also cover applications to a number of topics of current interest, including the debate on globalization, free trade agreements, the U.S. current account deficit, the medium run prospects for exchange rates, European integration, and the debate on global financial architecture following the financial crises in East Asia and Argentina. This course is an introduction to the theory of international trade and finance with applications to current policy issues. In this course we will cover the basic tools to understand what determines the flow of goods across countries, i.e. international trade, and what determines the flow of savings and investments from one country to another, i.e. international finance. We will also cover applications to a number of topics of current interest, including the debate on globalization, free trade agreements, the U.S. current account deficit, the medium run prospects for exchange rates, European integration, and the debate on global financial architecture following the financial crises in East Asia and Argentina.Subjects

theory of international trade | theory of international trade | finance | finance | policy | policy | flow of goods | flow of goods | flow of savings and investments | flow of savings and investments | globalization | globalization | free trade agreements | free trade agreements | the US current account deficit | the US current account deficit | exchange rates | exchange rates | European integration | European integration | global financial architecture | global financial architecture | financial crises | financial crises | East Asia | East Asia | Argentina | ArgentinaLicense

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 metadata14.121 Microeconomic Theory I (MIT) 14.121 Microeconomic Theory I (MIT)

Description

This course provides an introduction to microeconomic theory and is the first course in the microeconomic theory series. It is intended for graduate students in the economics program. Some components of the course are designed to teach material that all graduate students should know while others are used to introduce methodologies. Topics of recent interest will also be covered and may include: theories of production and individual choice (under certainty and uncertainty); markets and competition; tools of comparative statics and their application to price theory. This course provides an introduction to microeconomic theory and is the first course in the microeconomic theory series. It is intended for graduate students in the economics program. Some components of the course are designed to teach material that all graduate students should know while others are used to introduce methodologies. Topics of recent interest will also be covered and may include: theories of production and individual choice (under certainty and uncertainty); markets and competition; tools of comparative statics and their application to price theory.Subjects

microeconomic theory | microeconomic theory | theories of production and individual choice (under certainty and uncertainty) | theories of production and individual choice (under certainty and uncertainty) | markets and competition | markets and competition | tools of comparative statics and their application to price theory | tools of comparative statics and their application to price 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 metadata15.082J Network Optimization (MIT) 15.082J Network Optimization (MIT)

Description

15.082J/6.855J/ESD.78J is a graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, and finance, as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. We will also consider other extensions of network flow problems. 15.082J/6.855J/ESD.78J is a graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, and finance, as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. We will also consider other extensions of network flow problems.Subjects

15.082 | 15.082 | 6.855 | 6.855 | ESD.78 | ESD.78 | network models | network models | network design | network design | maximum flow algorithm | maximum flow algorithm | minimum cost flow | minimum cost flow | shortest path algorithm | shortest path algorithm | algorithm efficiency | algorithm efficiency | preflow push algorithm | preflow push algorithm | data structures | data structuresLicense

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 metadata15.072J Queues: Theory and Applications (MIT) 15.072J Queues: Theory and Applications (MIT)

Description

This class deals with the modeling and analysis of queueing systems, with applications in communications, manufacturing, computers, call centers, service industries and transportation. Topics include birth-death processes and simple Markovian queues, networks of queues and product form networks, single and multi-server queues, multi-class queueing networks, fluid models, adversarial queueing networks, heavy-traffic theory and diffusion approximations. The course will cover state of the art results which lead to research opportunities. This class deals with the modeling and analysis of queueing systems, with applications in communications, manufacturing, computers, call centers, service industries and transportation. Topics include birth-death processes and simple Markovian queues, networks of queues and product form networks, single and multi-server queues, multi-class queueing networks, fluid models, adversarial queueing networks, heavy-traffic theory and diffusion approximations. The course will cover state of the art results which lead to research opportunities.Subjects

modeling | modeling | queueing | queueing | queues | queues | queueing systems | queueing systems | communications | communications | manufacturing | manufacturing | computers | computers | call centers | call centers | service industries | service industries | transportation | transportation | applications | applications | birth-death processes | birth-death processes | markovian queues | markovian queues | networks | networks | single-server | single-server | multi-server | multi-server | multi-class queueing | multi-class queueing | fluid models | fluid models | adversarial queueing | adversarial queueing | heavy-traffic theory | heavy-traffic theory | diffusion approximations | diffusion approximationsLicense

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 metadata15.904 Strategic Management II (MIT) 15.904 Strategic Management II (MIT)

Description

This course is intended to be an extension of course 15.902, Strategic Management I, with the purpose of allowing the students to experience an in-depth application of the concepts and frameworks of strategic management. Throughout the course, Prof. Arnoldo Hax will discuss the appropriate methodologies, concepts, and tools pertinent to strategic analyses and will illustrate their use by discussing many applications in real-life settings, drawn from his own personal experiences. This course is intended to be an extension of course 15.902, Strategic Management I, with the purpose of allowing the students to experience an in-depth application of the concepts and frameworks of strategic management. Throughout the course, Prof. Arnoldo Hax will discuss the appropriate methodologies, concepts, and tools pertinent to strategic analyses and will illustrate their use by discussing many applications in real-life settings, drawn from his own personal experiences.Subjects

strategic management | strategic management | delta project | delta project | corporate | corporate | business | business | functional strategies | functional strategies | business management | business management | business processes | business processes | efficiency | efficiency | business model | business model | strategic planning | strategic planningLicense

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 metadata15.561 Information Technology Essentials (MIT) 15.561 Information Technology Essentials (MIT)

Description

This class offers a broad coverage of technology concepts and trends underlying current and future developments in information technology, and fundamental principles for the effective use of computer-based information systems. There will be a special emphasis on networks and distributed computing, including the World Wide Web. Other topics include: hardware and operating systems, software development tools and processes, relational databases, security and cryptography, enterprise applications, and electronic commerce. Hands-on exposure to Web, database, and graphical user interface (GUI) tools. This course is intended for students with little or no background in computer technology. Students with extensive education or work experience in computer technology should consider taking a more a This class offers a broad coverage of technology concepts and trends underlying current and future developments in information technology, and fundamental principles for the effective use of computer-based information systems. There will be a special emphasis on networks and distributed computing, including the World Wide Web. Other topics include: hardware and operating systems, software development tools and processes, relational databases, security and cryptography, enterprise applications, and electronic commerce. Hands-on exposure to Web, database, and graphical user interface (GUI) tools. This course is intended for students with little or no background in computer technology. Students with extensive education or work experience in computer technology should consider taking a more aSubjects

technology concepts | technology concepts | information technology | information technology | IT | IT | IS | IS | computer-based systems | computer-based systems | networks | networks | distributed computing | distributed computing | WWW | WWW | hardware | hardware | software tools | software tools | relational databases | relational databases | security | security | cryptography | cryptography | enterprise applications | enterprise applicationsLicense

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 metadata15.057 Systems Optimization (MIT) 15.057 Systems Optimization (MIT)

Description

Managers and engineers are constantly attempting to optimize, particularly in the design and operation of complex systems. This course is an application-oriented introduction to (systems) optimization. It seeks to: Motivate the use of optimization models to support managers and engineers in a wide variety of decision making situations; Show how several application domains (industries) use optimization; Introduce optimization modeling and solution techniques (including linear, non-linear, integer, and network optimization, and heuristic methods); Provide tools for interpreting and analyzing model-based solutions (sensitivity and post-optimality analysis, bounding techniques); and Develop the skills required to identify the opportunity and manage the implementation of an optimization-based Managers and engineers are constantly attempting to optimize, particularly in the design and operation of complex systems. This course is an application-oriented introduction to (systems) optimization. It seeks to: Motivate the use of optimization models to support managers and engineers in a wide variety of decision making situations; Show how several application domains (industries) use optimization; Introduce optimization modeling and solution techniques (including linear, non-linear, integer, and network optimization, and heuristic methods); Provide tools for interpreting and analyzing model-based solutions (sensitivity and post-optimality analysis, bounding techniques); and Develop the skills required to identify the opportunity and manage the implementation of an optimization-basedSubjects

system optimization | system optimization | distribution | distribution | production planning | production planning | supply chain managment | supply chain managment | scheduling | inventory | scheduling | inventory | scheduling | scheduling | inventory | inventoryLicense

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 metadata16.225 Computational Mechanics of Materials (MIT) 16.225 Computational Mechanics of Materials (MIT)

Description

16.225 is a graduate level course on Computational Mechanics of Materials. The primary focus of this course is on the teaching of state-of-the-art numerical methods for the analysis of the nonlinear continuum response of materials. The range of material behavior considered in this course includes: linear and finite deformation elasticity, inelasticity and dynamics. Numerical formulation and algorithms include: variational formulation and variational constitutive updates, finite element discretization, error estimation, constrained problems, time integration algorithms and convergence analysis. There is a strong emphasis on the (parallel) computer implementation of algorithms in programming assignments. The application to real engineering applications and problems in engineering science is 16.225 is a graduate level course on Computational Mechanics of Materials. The primary focus of this course is on the teaching of state-of-the-art numerical methods for the analysis of the nonlinear continuum response of materials. The range of material behavior considered in this course includes: linear and finite deformation elasticity, inelasticity and dynamics. Numerical formulation and algorithms include: variational formulation and variational constitutive updates, finite element discretization, error estimation, constrained problems, time integration algorithms and convergence analysis. There is a strong emphasis on the (parallel) computer implementation of algorithms in programming assignments. The application to real engineering applications and problems in engineering science isSubjects

Computational Mechanics | Computational Mechanics | Computation | Computation | Mechanics | Mechanics | Materials | Materials | Numerical Methods | Numerical Methods | Numerical | Numerical | Nonlinear Continuum Response | Nonlinear Continuum Response | Continuum | Continuum | Deformation | Deformation | Elasticity | Elasticity | Inelasticity | Inelasticity | Dynamics | Dynamics | Variational Formulation | Variational Formulation | Variational Constitutive Updates | Variational Constitutive Updates | Finite Element | Finite Element | Discretization | Discretization | Error Estimation | Error Estimation | Constrained Problems | Constrained Problems | Time Integration | Time Integration | Convergence Analysis | Convergence Analysis | Programming | Programming | Continuum Response | Continuum Response | Computational | Computational | state-of-the-art | state-of-the-art | methods | methods | modeling | modeling | simulation | simulation | mechanical | mechanical | response | response | engineering | engineering | aerospace | aerospace | civil | civil | material | material | science | science | biomechanics | biomechanics | behavior | behavior | finite | finite | deformation | deformation | elasticity | elasticity | inelasticity | inelasticity | contact | contact | friction | friction | coupled | coupled | numerical | numerical | formulation | formulation | algorithms | algorithms | Variational | Variational | constitutive | constitutive | updates | updates | element | element | discretization | discretization | mesh | mesh | generation | generation | error | error | estimation | estimation | constrained | constrained | problems | problems | time | time | convergence | convergence | analysis | analysis | parallel | parallel | computer | computer | implementation | implementation | programming | programming | assembly | assembly | equation-solving | equation-solving | formulating | formulating | implementing | implementing | complex | complex | approximations | approximations | equations | equations | motion | motion | dynamic | dynamic | deformations | deformations | continua | continua | plasticity | plasticity | rate-dependency | rate-dependency | integration | integrationLicense

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 metadata18.100A Introduction to Analysis (MIT) 18.100A Introduction to Analysis (MIT)

Description

Analysis I (18.100) in its various versions covers fundamentals of mathematical analysis: continuity, differentiability, some form of the Riemann integral, sequences and series of numbers and functions, uniform convergence with applications to interchange of limit operations, some point-set topology, including some work in Euclidean n-space. MIT students may choose to take one of three versions of 18.100: Option A (18.100A) chooses less abstract definitions and proofs, and gives applications where possible. Option B (18.100B) is more demanding and for students with more mathematical maturity; it places more emphasis from the beginning on point-set topology and n-space, whereas Option A is concerned primarily with analysis on the real line, saving for the last weeks work in 2-space (the pla Analysis I (18.100) in its various versions covers fundamentals of mathematical analysis: continuity, differentiability, some form of the Riemann integral, sequences and series of numbers and functions, uniform convergence with applications to interchange of limit operations, some point-set topology, including some work in Euclidean n-space. MIT students may choose to take one of three versions of 18.100: Option A (18.100A) chooses less abstract definitions and proofs, and gives applications where possible. Option B (18.100B) is more demanding and for students with more mathematical maturity; it places more emphasis from the beginning on point-set topology and n-space, whereas Option A is concerned primarily with analysis on the real line, saving for the last weeks work in 2-space (the plaSubjects

mathematical analysis | mathematical analysis | estimations | estimations | limit of a sequence | limit of a sequence | limit theorems | limit theorems | subsequences | subsequences | cluster points | cluster points | infinite series | infinite series | power series | power series | local and global properties | local and global properties | continuity | continuity | intermediate-value theorem | intermediate-value theorem | convexity | convexity | integrability | integrability | Riemann integral | Riemann integral | calculus | calculus | convergence | convergence | Gamma function | Gamma function | Stirling | Stirling | quantifiers and negation | quantifiers and negation | Leibniz | Leibniz | Fubini | Fubini | improper integrals | improper integrals | Lebesgue integral | Lebesgue integral | mathematical proofs | mathematical proofs | differentiation | differentiation | integration | integrationLicense

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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