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6.845 Quantum Complexity Theory (MIT) 6.845 Quantum Complexity Theory (MIT)

Description

This course is an introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs, advice, and interactive proof systems in the quantum world. The objective is to bring students to the research frontier. This course is an introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs, advice, and interactive proof systems in the quantum world. The objective is to bring students to the research frontier.Subjects

quantum computational complexity theory | quantum computational complexity theory | quantum computers | quantum computers | complexity classes | complexity classes | lower bounds | lower bounds | communication complexity | communication complexity | interactive proof systems | interactive proof systems | BQP | BQP | quantum algorithms | quantum algorithms | QMA | QMA | quantum Merlin Arthur | quantum Merlin ArthurLicense

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

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This course is 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

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This course introduces basic mathematical models of computation and the finite representation of infinite objects. 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 introduces basic mathematical models of computation and the finite representation of infinite objects. 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

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See all metadata6.033 Computer System Engineering (MIT) 6.033 Computer System Engineering (MIT)

Description

Includes audio/video content: AV lectures. This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Two design projects are required, and students engage in extensive written communication exercises. Includes audio/video content: AV lectures. This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Two design projects are required, and students engage in extensive written communication exercises.Subjects

computer systems | computer systems | systems design | systems design | complexity | complexity | abstractions | abstractions | modularity | modularity | client server | client server | operating system | operating system | performance | performance | networks | networks | layering | layering | routing | routing | congestion control | congestion control | reliability | reliability | atomicity | atomicity | isolation | isolation | security | security | authentication | authentication | cryptography | cryptography | therac 25 | therac 25 | unix | unix | mapreduce | mapreduce | architecture of complexity | architecture of complexity | trusting trust | trusting trust | computer system design | computer system designLicense

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This course studies what makes a good design and how one develops a good design. Students consider how the design of engineered systems (such as hardware, software, materials, and manufacturing systems) differ from the "design" of natural systems such as biological systems; discuss complexity and how one makes use of complexity theory to improve design; and discover how one uses axiomatic design theory (AD theory) in design of many different kinds of engineered systems. Questions are analyzed using Axiomatic Design Theory and Complexity Theory. Case studies are presented including the design of machines, tribological systems, materials, manufacturing systems, and recent inventions. Implications of AD and complexity theories on biological systems discussed. This course studies what makes a good design and how one develops a good design. Students consider how the design of engineered systems (such as hardware, software, materials, and manufacturing systems) differ from the "design" of natural systems such as biological systems; discuss complexity and how one makes use of complexity theory to improve design; and discover how one uses axiomatic design theory (AD theory) in design of many different kinds of engineered systems. Questions are analyzed using Axiomatic Design Theory and Complexity Theory. Case studies are presented including the design of machines, tribological systems, materials, manufacturing systems, and recent inventions. Implications of AD and complexity theories on biological systems discussed.Subjects

information content | information content | electrical connector | electrical connector | constraint | constraint | complexity | complexity | manufacturing | manufacturing | design | design | functional requirement | functional requirement | requirement | requirement | tradeoff | tradeoff | optimization | optimization | engineered systems | engineered systems | natural systems | natural systems | complexity theory | complexity theory | axiomatic design | axiomatic design | tribology | tribology | tribological systems | tribological systems | manufacturing systems | manufacturing systems | biological systems | biological systemsLicense

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See all metadata15.099 Readings in Optimization (MIT) 15.099 Readings in Optimization (MIT)

Description

In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention. In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.Subjects

deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization | deterministic optimization | algorithms | algorithms | stochastic processes | stochastic processes | random number generation | random number generation | simplex method | simplex method | nonlinear | nonlinear | convex | convex | complexity analysis | complexity analysis | semidefinite programming | semidefinite programming | heuristic | heuristic | global optimization | global optimization | Las Vegas algorithm | Las Vegas algorithm | randomized algorithm | randomized algorithm | linear programming | linear programming | search techniques | search techniques | hit and run | hit and run | NP-hard | NP-hard | approximation | approximationLicense

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See all metadataReadme file for Computer Science Concepts

Description

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

ukoer | strings lecture | induction and recursion lecture | induction lecture | recursion lecture | complexity lecture | languages lecture | computer sciences concepts test | computer science concepts test | computer science concepts assignment | computer science concepts practical | introduction | computer science concepts | computer science concept | computer science | strings and languages | strings and language | string and languages | string and language | string | language | languages | finite automata | automata | finite | push down automata | push down | prolog | data structures and algorithms | data structure and algorithms | data structures and algorithm | data structure and algorithm | data structures | data structure | algorithms | algorithm | revision exercises | revision | induction and recursion | induction | recursion | turing machines | turing machine | turing | machine | machines | complexity | grammar | grammar and languages | grammar and language | introduction lecture | computer science concepts lecture | computer science concept lecture | computer science lecture | strings and languages lecture | strings and language lecture | string and languages lecture | string and language lecture | string lecture | language lecture | finite automata lecture | automata lecture | finite lecture | push down automata lecture | push down lecture | prolog lecture | data structures and algorithms lecture | data structure and algorithms lecture | data structures and algorithm lecture | data structure and algorithm lecture | data structures lecture | data structure lecture | algorithms lecture | algorithm lecture | revision exercises lecture | revision lecture | turing machines lecture | turing machine lecture | turing lecture | machine lecture | machines lecture | computer science class test | computer science concept class test | computer science concepts class test | strings and languages class test | strings and language class test | string and languages class test | string and language class test | string class test | language class test | languages class test | introduction class test | grammar lecture | grammar and languages lecture | grammar and language lecture | computer science assignment | computer science concept assignment | strings and languages assignment | strings and language assignment | string and languages assignment | string and language assignment | string assignment | language assignment | languages assignment | finite automata class test | automata class test | finite class test | finite automata assignment | automata assignment | finite assignment | push down automata class test | push down class test | push down automata assignment | push down assignment | prolog class test | data structures and algorithms class test | data structure and algorithms class test | data structures and algorithm class test | data structure and algorithm class test | data structures class test | data structure class test | algorithms class test | algorithm class test | computer science practical | computer science concept practical | data structures and algorithms practical | data structure and algorithms practical | data structures and algorithm practical | data structure and algorithm practical | data structures practical | data structure practical | algorithms practical | algorithm practical | revision exercises class test | revision class test | induction and recursion class test | induction class test | recursion class test | induction and recursion assignment | induction assignment | recursion assignment | turing machines class test | turing machine class test | turing class test | machine class test | machines class test | turing machines assignment | turing machine assignment | turing assignment | machine assignment | machines assignment | complexity class test | grammar class test | grammar and languages class test | grammar and language class test | Computer science | I100License

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See all metadata6.845 Quantum Complexity Theory (MIT)

Description

This course is an introduction to quantum computational complexity theory, the study of the fundamental capabilities and limitations of quantum computers. Topics include complexity classes, lower bounds, communication complexity, proofs, advice, and interactive proof systems in the quantum world. The objective is to bring students to the research frontier.Subjects

quantum computational complexity theory | quantum computers | complexity classes | lower bounds | communication complexity | interactive proof systems | BQP | quantum algorithms | QMA | quantum Merlin ArthurLicense

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In establishing the Engineering Systems Division, MIT has embarked on a bold experiment – bringing together diverse areas of expertise into what is designed to be a new field of study. In many respects, the full scale and scope of Engineering Systems as a field is still emerging. This seminar is simultaneously designed to codify what we presently know and to give direction for future development. In establishing the Engineering Systems Division, MIT has embarked on a bold experiment – bringing together diverse areas of expertise into what is designed to be a new field of study. In many respects, the full scale and scope of Engineering Systems as a field is still emerging. This seminar is simultaneously designed to codify what we presently know and to give direction for future development.Subjects

engineering systems | engineering systems | complexity | complexity | uncertainty | uncertainty | fragility | fragility | robustness | robustness | systems engineering | systems engineering | systems dynamics | systems dynamics | agent modeling | agent modeling | systems simulations | systems simulations | large-scale systems change | large-scale systems change | modeling paradigms | modeling paradigms | cumulative knowledge | cumulative knowledge | empirical data generation | empirical data generation | boundary setting | boundary setting | network models | network models | policy evaluation | policy evaluationLicense

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See all metadata21L.448J Darwin and Design (MIT) 21L.448J Darwin and Design (MIT)

Description

In the Origin of Species (1859), Charles Darwin gave us a model for understanding how natural objects and systems can evidence design without positing a designer: how purpose and mechanism can exist without intelligent agency. Texts in this course deal with pre- and post-Darwinian treatment of this topic within literature and speculative thought since the eighteenth century. We will give some attention to the modern study of feedback mechanisms in artificial intelligence. Our reading will be in Hume, Voltaire, Malthus, Darwin, Butler, H. G. Wells, and Turing. In the Origin of Species (1859), Charles Darwin gave us a model for understanding how natural objects and systems can evidence design without positing a designer: how purpose and mechanism can exist without intelligent agency. Texts in this course deal with pre- and post-Darwinian treatment of this topic within literature and speculative thought since the eighteenth century. We will give some attention to the modern study of feedback mechanisms in artificial intelligence. Our reading will be in Hume, Voltaire, Malthus, Darwin, Butler, H. G. Wells, and Turing.Subjects

Origin of Species | Origin of Species | Darwin | Darwin | intelligent agency | intelligent agency | literature | literature | speculative thought | speculative thought | eighteenth century | eighteenth century | feedback mechanism | feedback mechanism | artificial intelligence | artificial intelligence | Hume | Hume | Voltaire | Voltaire | Malthus | Malthus | Butler | Butler | Hardy | Hardy | H.G. Wells | H.G. Wells | Freud | Freud | Evolution | Evolution | Modern Western philosophy | Modern Western philosophy | Philosophy of science | Philosophy of science | Religion | Religion | Science | Science | Life Sciences | Life Sciences | Social Aspects | Social Aspects | History | History | Intelligent design | individual species | Intelligent design | individual species | complexity | complexity | development | development | God theory of evolution | God theory of evolution | science | science | theological explanation | theological explanation | universe | universe | creatures | creatures | faith | faith | and theology | and theology | purpose of evolution | purpose of evolution | Design | Design | models | models | adaptation | adaptationLicense

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Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard pr Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard prSubjects

performance engineering | performance engineering | parallelism | parallelism | computational power | computational power | complexity | complexity | computation | computation | efficiency | efficiency | high performance | high performance | software system | software system | performance analysis | performance analysis | algorithms | algorithms | instruction level optimization | instruction level optimization | cache | cache | memory | memory | parallel programming | parallel programming | distributed systems | distributed systems | algorithmic design | algorithmic design | profile | profile | multithreaded | multithreaded | cilk | cilk | cilk arts | cilk arts | ray tracer | ray tracer | render | renderLicense

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See all metadataESD.84 Engineering Systems Doctoral Seminar (MIT) ESD.84 Engineering Systems Doctoral Seminar (MIT)

Description

Examines core theory and contextual applications of the emerging field of Engineering Systems. The focus is on doctoral-level analysis of scholarship on key concepts such as complexity, uncertainty, fragility, and robustness, as well as a critical look at the historical roots of the field and related areas such as systems engineering, systems dynamics, agent modeling, and systems simulations. Contextual applications range from aerospace to technology implementation to regulatory systems to large-scale systems change. Special attention is given to the interdependence of social and technical dimensions of engineering systems. Examines core theory and contextual applications of the emerging field of Engineering Systems. The focus is on doctoral-level analysis of scholarship on key concepts such as complexity, uncertainty, fragility, and robustness, as well as a critical look at the historical roots of the field and related areas such as systems engineering, systems dynamics, agent modeling, and systems simulations. Contextual applications range from aerospace to technology implementation to regulatory systems to large-scale systems change. Special attention is given to the interdependence of social and technical dimensions of engineering systems.Subjects

engineering systems | engineering systems | complexity | complexity | fragility | fragility | robustness | robustness | systems engineering | systems engineering | systems dynamics | systems dynamics | agent modeling | agent modeling | systems simulations | systems simulations | large-scale systems change | large-scale systems change | uncertainty | uncertaintyLicense

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This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. We will also look at case studies of working systems and readings from the current literature provide comparisons and contrasts, and do two design projects. Students engage in extensive written communication exercises. Enrollment may be limited. This course is worth 4 Engineering Design Points.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5501 (Computer System Engineering). This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. We will also look at case studies of working systems and readings from the current literature provide comparisons and contrasts, and do two design projects. Students engage in extensive written communication exercises. Enrollment may be limited. This course is worth 4 Engineering Design Points.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5501 (Computer System Engineering).Subjects

computer software | computer software | hardware systems | hardware systems | controlling complexity | controlling complexity | strong modularity | strong modularity | client-server design | client-server design | virtual memory | virtual memory | threads | threads | networks | networks | atomicity | atomicity | coordination | coordination | parallel activities | parallel activities | recovery | recovery | reliability | reliability | privacy | privacy | security | security | encryption | encryption | impact on society | impact on society | computer systems | computer systems | case studies | case studiesLicense

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See all metadata18.404J Theory of Computation (MIT) 18.404J Theory of Computation (MIT)

Description

A more extensive and theoretical treatment of the material in 18.400J, Automata, Computability, and Complexity, emphasizing computability and computational complexity theory. Regular and context-free languages. Decidable and undecidable problems, reducibility, recursive function theory. Time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems. A more extensive and theoretical treatment of the material in 18.400J, Automata, Computability, and Complexity, emphasizing computability and computational complexity theory. Regular and context-free languages. Decidable and undecidable problems, reducibility, recursive function theory. Time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems.Subjects

computability | computability | computational complexity theory | computational complexity theory | Regular and context-free languages | Regular and context-free languages | Decidable and undecidable problems | Decidable and undecidable problems | reducibility | reducibility | recursive function theory | recursive function theory | Time and space measures on computation | Time and space measures on computation | completeness | completeness | hierarchy theorems | hierarchy theorems | inherently complex problems | inherently complex problems | oracles | oracles | probabilistic computation | probabilistic computation | interactive proof systems | interactive proof systems | 18.404 | 18.404 | 6.840 | 6.840License

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See all metadata3.012 Fundamentals of Materials Science (MIT) 3.012 Fundamentals of Materials Science (MIT)

Description

This subject describes the fundamentals of bonding, energetics, and structure that underpin materials science. From electrons to silicon to DNA: the role of electronic bonding in determining the energy, structure, and stability of materials. Quantum mechanical descriptions of interacting electrons and atoms. Symmetry properties of molecules and solids. Structure of complex and disordered materials. Introduction to thermodynamic functions and laws governing equilibrium properties, relating macroscopic behavior to molecular models of materials. Develops basis for understanding a broad range of materials phenomena, from heat capacities, phase transformations, and multiphase equilibria to chemical reactions and magnetism. Fundamentals are taught using real-world examples such as engineered all This subject describes the fundamentals of bonding, energetics, and structure that underpin materials science. From electrons to silicon to DNA: the role of electronic bonding in determining the energy, structure, and stability of materials. Quantum mechanical descriptions of interacting electrons and atoms. Symmetry properties of molecules and solids. Structure of complex and disordered materials. Introduction to thermodynamic functions and laws governing equilibrium properties, relating macroscopic behavior to molecular models of materials. Develops basis for understanding a broad range of materials phenomena, from heat capacities, phase transformations, and multiphase equilibria to chemical reactions and magnetism. Fundamentals are taught using real-world examples such as engineered allSubjects

fundamentals of bonding | energetics | and structure | fundamentals of bonding | energetics | and structure | Quantum mechanical descriptions of interacting electrons and atoms | Quantum mechanical descriptions of interacting electrons and atoms | Symmetry properties of molecules and solids | Symmetry properties of molecules and solids | complex and disordered materials | complex and disordered materials | thermodynamic functions | thermodynamic functions | equilibrium properties | equilibrium properties | macroscopic behavior | macroscopic behavior | molecular models | molecular models | heat capacities | heat capacities | phase transformations | phase transformations | multiphase equilibria | multiphase equilibria | chemical reactions | chemical reactions | magnetism | magnetism | engineered alloys | engineered alloys | electronic and magnetic materials | electronic and magnetic materials | ionic and network solids | ionic and network solids | polymers | polymers | biomaterials | biomaterials | energetics | energetics | structure | structure | materials science | materials science | electrons | electrons | silicon | silicon | DNA | DNA | electronic bonding | electronic bonding | energy | energy | stability | stability | quantum mechanics | quantum mechanics | atoms | atoms | interactions | interactions | symmetry | symmetry | molecules | molecules | solids | solids | complex material | complex material | disorderd materials | disorderd materials | thermodynamic laws | thermodynamic laws | electronic materials | electronic materials | magnetic materials | magnetic materials | ionic solids | ionic solids | network solids | network solids | statistical mechanics | statistical mechanics | microstates | microstates | microscopic complexity | microscopic complexity | entropy | entropyLicense

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This field seminar in international political economy covers major theoretical, empirical, and policy perspectives. The basic orientation is disciplinary and comparative (over time and across countries, regions, firms), spanning issues relevant to both industrial and developing states. Special attention is given to challenges and dilemmas shaped by the macro-level consequences of micro-level behavior, and by micro-level adjustments to macro-level influences. This field seminar in international political economy covers major theoretical, empirical, and policy perspectives. The basic orientation is disciplinary and comparative (over time and across countries, regions, firms), spanning issues relevant to both industrial and developing states. Special attention is given to challenges and dilemmas shaped by the macro-level consequences of micro-level behavior, and by micro-level adjustments to macro-level influences.Subjects

international relations | international relations | political science | political science | economics | economics | wealth | wealth | neoclassical | neoclassical | development | development | ecology | ecology | power | power | trade | trade | capital | capital | foreign investment | foreign investment | intellectual property | intellectual property | migration | migration | foreignpolicy | foreignpolicy | globalization | globalization | internet | internet | sustainability | sustainability | institutions | institutions | foreign policy | foreign policy | IPE | IPE | dual national objectives | dual national objectives | global context | global context | pursuit of power | pursuit of power | pursuit of wealth | pursuit of wealth | international political economy | international political economy | neoclassical economics | neoclassical economics | development economics | development economics | ecological economics | ecological economics | lateral pressure | lateral pressure | perspectives | perspectives | structural views | structural views | power relations | power relations | politics | politics | international trade | international trade | capital flows | capital flows | intellectual property rights | intellectual property rights | international migration | international migration | foreign economic policy | foreign economic policy | international economic institutions | international economic institutions | theoretical perspectives | theoretical perspectives | empirical perspectives | empirical perspectives | policy perspectives | policy perspectives | disciplinary | disciplinary | comparative | comparative | time | time | countries | countries | regions | regions | firms | firms | industrial states | industrial states | developing states | developing states | macro-level consequences | macro-level consequences | micro-level behavior | micro-level behavior | micro-level adjustments | micro-level adjustments | macro-level influences | macro-level influences | complexity | complexity | localization | localization | technology | technology | knowledge economy | knowledge economy | finance | finance | global markets | global markets | political economy | political economy | e-commerce | e-commerceLicense

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 metadata21L.448J Darwin and Design (MIT) 21L.448J Darwin and Design (MIT)

Description

Includes audio/video content: AV lectures. Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology. Includes audio/video content: AV lectures. Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.Subjects

21L.448 | 21L.448 | 21W.739 | 21W.739 | Origin of Species | Origin of Species | Darwin | Darwin | intelligent agency | intelligent agency | literature | literature | speculative thought | speculative thought | eighteenth century | eighteenth century | feedback mechanism | feedback mechanism | artificial intelligence | artificial intelligence | Hume | Hume | Voltaire | Voltaire | Malthus | Malthus | Butler | Butler | Hardy | Hardy | H.G. Wells | H.G. Wells | Freud | Freud | Evolution | Evolution | Modern Western philosophy | Modern Western philosophy | Philosophy of science | Philosophy of science | Religion | Religion | Science | Science | Life Sciences | Life Sciences | Social Aspects | Social Aspects | History | History | Intelligent design | individual species | Intelligent design | individual species | complexity | complexity | development | development | God theory of evolution | God theory of evolution | science | science | theological explanation | theological explanation | universe | universe | creatures | creatures | faith | faith | and theology | and theology | purpose of evolution | purpose of evolution | Design | Design | models | models | adaptation | adaptationLicense

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 metadataESD.932 Engineering Ethics (MIT) ESD.932 Engineering Ethics (MIT)

Description

Includes audio/video content: AV lectures, AV faculty introductions. This course introduces the theory and the practice of engineering ethics using a multi-disciplinary and cross-cultural approach. Theory includes ethics and philosophy of engineering. Historical cases are taken primarily from the scholarly literatures on engineering ethics, and hypothetical cases are written by students. Each student will write a story by selecting an ancestor or mythic hero as a substitute for a character in a historical case. Students will compare these cases and recommend action. Includes audio/video content: AV lectures, AV faculty introductions. This course introduces the theory and the practice of engineering ethics using a multi-disciplinary and cross-cultural approach. Theory includes ethics and philosophy of engineering. Historical cases are taken primarily from the scholarly literatures on engineering ethics, and hypothetical cases are written by students. Each student will write a story by selecting an ancestor or mythic hero as a substitute for a character in a historical case. Students will compare these cases and recommend action.Subjects

philosophy | philosophy | myth | myth | Kant | Kant | John Stuart Mill | John Stuart Mill | Kierkegaard | Kierkegaard | Augustine | Augustine | Joseph Campbell | Joseph Campbell | risk | risk | disaster | disaster | honesty | honesty | whisteblower | whisteblower | social responsibility | social responsibility | Pugwash | Pugwash | environment | environment | bioethics | bioethics | lawsuit | lawsuit | praxistics | praxistics | decision making | decision making | management | management | accident | accident | choice | choice | morals | morals | complexity | complexity | judgement | judgement | consequence | consequenceLicense

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 metadata2.76 Multi-Scale System Design (MIT) 2.76 Multi-Scale System Design (MIT)

Description

Multi-scale systems (MuSS) consist of components from two or more length scales (nano, micro, meso, or macro-scales). In MuSS, the engineering modeling, design principles, and fabrication processes of the components are fundamentally different. The challenge is to make these components so they are conceptually and model-wise compatible with other-scale components with which they interface. This course covers the fundamental properties of scales, design theories, modeling methods and manufacturing issues which must be addressed in these systems. Examples of MuSS include precision instruments, nanomanipulators, fiber optics, micro/nano-photonics, nanorobotics, MEMS (piezoelectric driven manipulators and optics), X-Ray telescopes and carbon nano-tube assemblies. Students master the materials Multi-scale systems (MuSS) consist of components from two or more length scales (nano, micro, meso, or macro-scales). In MuSS, the engineering modeling, design principles, and fabrication processes of the components are fundamentally different. The challenge is to make these components so they are conceptually and model-wise compatible with other-scale components with which they interface. This course covers the fundamental properties of scales, design theories, modeling methods and manufacturing issues which must be addressed in these systems. Examples of MuSS include precision instruments, nanomanipulators, fiber optics, micro/nano-photonics, nanorobotics, MEMS (piezoelectric driven manipulators and optics), X-Ray telescopes and carbon nano-tube assemblies. Students master the materialsSubjects

scale | scale | complexity | complexity | nano | micro | meso | or macro-scale | nano | micro | meso | or macro-scale | kinematics | kinematics | metrology | metrology | engineering modeling | motion | engineering modeling | motion | modeling | modeling | design | design | manufacture | manufacture | design principles | design principles | fabrication process | fabrication process | functional requirements | functional requirements | precision instruments | precision instruments | nanomanipulators | fiber optics | micro- photonics | nano-photonics | nanorobotics | MEMS | nanomanipulators | fiber optics | micro- photonics | nano-photonics | nanorobotics | MEMS | piezoelectric | transducer | actuator | sensor | piezoelectric | transducer | actuator | sensor | constraint | rigid constraint | flexible constraint | ride-flexible constraint | constraint | rigid constraint | flexible constraint | ride-flexible constraint | constaint-based design | constaint-based design | carbon nanotube | carbon nanotube | nanowire | nanowire | scanning tunneling microscope | scanning tunneling microscope | flexure | flexure | protein structure | protein structure | polymer structure | polymer structure | nanopelleting | nanopipette | nanowire | nanopelleting | nanopipette | nanowire | TMA pixel array | TMA pixel array | error modeling | error modeling | repeatability | repeatabilityLicense

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

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This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power. This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.Subjects

finite automata | finite automata | Turing machine | Turing machine | halting problem | halting problem | computability | computability | computational complexity | computational complexity | polynomial time | polynomial time | P | P | NP | NP | NP complete | NP complete | probabilistic algorithms | probabilistic algorithms | private-key cryptography | private-key cryptography | public-key cryptography | public-key cryptography | randomness | randomnessLicense

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

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This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity—with Euclid's algorithm and other ancient examples of computational thinking—the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, Gödel's theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and q This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity—with Euclid's algorithm and other ancient examples of computational thinking—the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, Gödel's theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and qSubjects

computer science | computer science | theoretical computer science | theoretical computer science | logic | logic | turing machines | turing machines | computability | computability | finite automata | finite automata | godel | godel | complexity | complexity | polynomial time | polynomial time | efficient algorithms | efficient algorithms | reducibility | reducibility | p and np | p and np | np completeness | np completeness | private key cryptography | private key cryptography | public key cryptography | public key cryptography | pac learning | pac learning | quantum computing | quantum computing | quantum algorithms | quantum algorithmsLicense

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

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This course teaches simple reasoning techniques for complex phenomena: divide and conquer, dimensional analysis, extreme cases, continuity, scaling, successive approximation, balancing, cheap calculus, and symmetry. Applications are drawn from the physical and biological sciences, mathematics, and engineering. Examples include bird and machine flight, neuron biophysics, weather, prime numbers, and animal locomotion. Emphasis is on low-cost experiments to test ideas and on fostering curiosity about phenomena in the world. This course teaches simple reasoning techniques for complex phenomena: divide and conquer, dimensional analysis, extreme cases, continuity, scaling, successive approximation, balancing, cheap calculus, and symmetry. Applications are drawn from the physical and biological sciences, mathematics, and engineering. Examples include bird and machine flight, neuron biophysics, weather, prime numbers, and animal locomotion. Emphasis is on low-cost experiments to test ideas and on fostering curiosity about phenomena in the world.Subjects

approximation | approximation | science | science | engineering | engineering | managing complexity | managing complexity | divide and conquer | divide and conquer | heterogeneous hierarchies | heterogeneous hierarchies | homogeneous hierarchies | homogeneous hierarchies | proportional reasoning | proportional reasoning | conservation/box models | conservation/box models | dimensional analysis | dimensional analysis | special cases | special cases | extreme cases | extreme cases | discretization | discretization | spring models | spring models | symmetry | symmetry | invariance | invariance | discarding information | discarding information | oil imports | oil imports | tree representations | tree representations | gold | gold | random walks | random walks | UNIX | UNIX | triangle bisection | triangle bisection | pentagonal heat flow | pentagonal heat flow | jump heights | jump heights | simple calculus | simple calculus | drag | drag | cycling | cycling | swimming | swimming | flying | flying | flight | flight | algebraic symmetry | algebraic symmetry | densities | densities | hydrogen size | hydrogen size | bending of light | bending of light | Buckingham Pi Theorem | Buckingham Pi Theorem | pulley acceleration | pulley acceleration | waves | wavesLicense

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This research-oriented course will focus on algebraic and computational techniques for optimization problems involving polynomial equations and inequalities with particular emphasis on the connections with semidefinite optimization. The course will develop in a parallel fashion several algebraic and numerical approaches to polynomial systems, with a view towards methods that simultaneously incorporate both elements. We will study both the complex and real cases, developing techniques of general applicability, and stressing convexity-based ideas, complexity results, and efficient implementations. Although we will use examples from several engineering areas, particular emphasis will be given to those arising from systems and control applications. This research-oriented course will focus on algebraic and computational techniques for optimization problems involving polynomial equations and inequalities with particular emphasis on the connections with semidefinite optimization. The course will develop in a parallel fashion several algebraic and numerical approaches to polynomial systems, with a view towards methods that simultaneously incorporate both elements. We will study both the complex and real cases, developing techniques of general applicability, and stressing convexity-based ideas, complexity results, and efficient implementations. Although we will use examples from several engineering areas, particular emphasis will be given to those arising from systems and control applications.Subjects

algebraic and computational techniques | algebraic and computational techniques | optimization problems | optimization problems | polynomial equations | polynomial equations | inequalities | inequalities | semidefinite optimization | semidefinite optimization | convexity-based ideas | convexity-based ideas | complexity results | complexity results | efficient implementations | efficient implementationsLicense

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

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See all metadata6.875 Cryptography and Cryptanalysis (MIT) 6.875 Cryptography and Cryptanalysis (MIT)

Description

This course features a rigorous introduction to modern cryptography, with an emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements. This course features a rigorous introduction to modern cryptography, with an emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements.Subjects

modern cryptography | modern cryptography | fundamental cryptographic primitives | fundamental cryptographic primitives | public-key encryption | public-key encryption | digital signatures | digital signatures | pseudo-random number generation | pseudo-random number generation | basic protocols | basic protocols | computational complexity | computational complexity | two-party protocols | two-party protocols | zero-knowledge | zero-knowledgeLicense

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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6.896 covers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5511 (Theory of Parallel Hardware). 6.896 covers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5511 (Theory of Parallel Hardware).Subjects

parallel hardware | parallel hardware | computer arithmetic | computer arithmetic | physical design | physical design | algorithms | algorithms | arithmetic circuits | arithmetic circuits | parallel prefix | parallel prefix | systolic arrays | systolic arrays | retiming | retiming | clocking methodologies | clocking methodologies | boolean logic | boolean logic | sorting networks | sorting networks | interconnection networks | interconnection networks | hypercubic networks | hypercubic networks | P-completeness | P-completeness | VLSI layout theory | VLSI layout theory | reconfigurable wiring | reconfigurable wiring | fat-trees | fat-trees | area-time complexity | area-time complexityLicense

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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