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6.856J Randomized Algorithms (MIT) 6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms. This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.

Subjects

Randomized Algorithms | Randomized Algorithms | algorithms | algorithms | efficient in time and space | efficient in time and space | randomization | randomization | computational problems | computational problems | data structures | data structures | graph algorithms | graph algorithms | optimization | optimization | geometry | geometry | Markov chains | Markov chains | sampling | sampling | estimation | estimation | geometric algorithms | geometric algorithms | parallel and distributed algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and ditributed algorithm | parallel and distributed algorithm | parallel and distributed algorithm | random sampling | random sampling | random selection of witnesses | random selection of witnesses | symmetry breaking | symmetry breaking | randomized computational models | randomized computational models | hash tables | hash tables | skip lists | skip lists | minimum spanning trees | minimum spanning trees | shortest paths | shortest paths | minimum cuts | minimum cuts | convex hulls | convex hulls | linear programming | linear programming | fixed dimension | fixed dimension | arbitrary dimension | arbitrary dimension | approximate counting | approximate counting | parallel algorithms | parallel algorithms | online algorithms | online algorithms | derandomization techniques | derandomization techniques | probabilistic analysis | probabilistic analysis | computational number theory | computational number theory | simplicity | simplicity | speed | speed | design | design | basic probability theory | basic probability theory | application | application | randomized complexity classes | randomized complexity classes | game-theoretic techniques | game-theoretic techniques | Chebyshev | Chebyshev | moment inequalities | moment inequalities | limited independence | limited independence | coupon collection | coupon collection | occupancy problems | occupancy problems | tail inequalities | tail inequalities | Chernoff bound | Chernoff bound | conditional expectation | conditional expectation | probabilistic method | probabilistic method | random walks | random walks | algebraic techniques | algebraic techniques | probability amplification | probability amplification | sorting | sorting | searching | searching | combinatorial optimization | combinatorial optimization | approximation | approximation | counting problems | counting problems | distributed algorithms | distributed algorithms | 6.856 | 6.856 | 18.416 | 18.416

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MAS.110 Fundamentals of Computational Media Design (MIT) MAS.110 Fundamentals of Computational Media Design (MIT)

Description

This class introduces principles of analysis and synthesis in the computational medium. Expressive examples that illustrate the intersection of computation with the traditional arts are developed on a weekly basis. Hands-on design exercises are continually framed and examined in the larger context of contemporary digital art. This class introduces principles of analysis and synthesis in the computational medium. Expressive examples that illustrate the intersection of computation with the traditional arts are developed on a weekly basis. Hands-on design exercises are continually framed and examined in the larger context of contemporary digital art.

Subjects

analysis | analysis | synthesis | synthesis | computational media | computational media | computational and traditional arts | computational and traditional arts | design | design | programming | programming | javascript | javascript | contemporary digital art | contemporary digital art | machine age | machine age | media design | media design | analog vs digital art | analog vs digital art | graphic design | graphic design | web design | web design | photography | photography | storytelling | storytelling | modern art | modern art | computation | computation | arts | arts | design exercises | design exercises | studio | studio | analog art | analog art

License

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16.100 Aerodynamics (MIT) 16.100 Aerodynamics (MIT)

Description

This course extends fluid mechanic concepts from Unified Engineering to the aerodynamic performance of wings and bodies in sub/supersonic regimes. 16.100 generally has four components: subsonic potential flows, including source/vortex panel methods; viscous flows, including laminar and turbulent boundary layers; aerodynamics of airfoils and wings, including thin airfoil theory, lifting line theory, and panel method/interacting boundary layer methods; and supersonic and hypersonic airfoil theory. Course material varies each year depending upon the focus of the design problem. Technical RequirementsFile decompression software, such as Winzip® or StuffIt®, is required to open the .tar files found on this course site. MATLAB&#1 This course extends fluid mechanic concepts from Unified Engineering to the aerodynamic performance of wings and bodies in sub/supersonic regimes. 16.100 generally has four components: subsonic potential flows, including source/vortex panel methods; viscous flows, including laminar and turbulent boundary layers; aerodynamics of airfoils and wings, including thin airfoil theory, lifting line theory, and panel method/interacting boundary layer methods; and supersonic and hypersonic airfoil theory. Course material varies each year depending upon the focus of the design problem. Technical RequirementsFile decompression software, such as Winzip® or StuffIt®, is required to open the .tar files found on this course site. MATLAB&#1

Subjects

aerodynamics | aerodynamics | airflow | airflow | air | air | body | body | aircraft | aircraft | aerodynamic modes | aerodynamic modes | aero | aero | forces | forces | flow | flow | computational | computational | CFD | CFD | aerodynamic analysis | aerodynamic analysis | lift | lift | drag | drag | potential flows | potential flows | imcompressible | imcompressible | supersonic | supersonic | subsonic | subsonic | panel method | panel method | vortex lattice method | vortex lattice method | boudary layer | boudary layer | transition | transition | turbulence | turbulence | inviscid | inviscid | viscous | viscous | euler | euler | navier-stokes | navier-stokes | wind tunnel | wind tunnel | flow similarity | flow similarity | non-dimensional | non-dimensional | mach number | mach number | reynolds number | reynolds number | integral momentum | integral momentum | airfoil | airfoil | wing | wing | stall | stall | friction drag | friction drag | induced drag | induced drag | wave drag | wave drag | pressure drag | pressure drag | fluid element | fluid element | shear strain | shear strain | normal strain | normal strain | vorticity | vorticity | divergence | divergence | substantial derviative | substantial derviative | laminar | laminar | displacement thickness | displacement thickness | momentum thickness | momentum thickness | skin friction | skin friction | separation | separation | velocity profile | velocity profile | 2-d panel | 2-d panel | 3-d vortex | 3-d vortex | thin airfoil | thin airfoil | lifting line | lifting line | aspect ratio | aspect ratio | twist | twist | camber | camber | wing loading | wing loading | roll moments | roll moments | finite volume approximation | finite volume approximation | shocks | shocks | expansion fans | expansion fans | shock-expansion theory | shock-expansion theory | transonic | transonic | critical mach number | critical mach number | wing sweep | wing sweep | Kutta condition | Kutta condition | team project | team project | blended-wing-body | blended-wing-body | computational fluid dynamics | computational fluid dynamics | Incompressible | Incompressible

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6.877J Computational Evolutionary Biology (MIT) 6.877J Computational Evolutionary Biology (MIT)

Description

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data. Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

Subjects

6.877 | 6.877 | HST.949 | HST.949 | computational approaches | computational approaches | evolutionary biology | evolutionary biology | evolutionary theory and inferential logic of evolution by natural selection | evolutionary theory and inferential logic of evolution by natural selection | computational and algorithmic implications and requirements of evolutionary models | computational and algorithmic implications and requirements of evolutionary models | whole-genome species comparison | whole-genome species comparison | phylogenetic tree construction | phylogenetic tree construction | molecular evolution | molecular evolution | homology and development | homology and development | optimization and evolvability | optimization and evolvability | heritability | heritability | disease evolution | disease evolution | detecting selection in human populations | and evolution of language | detecting selection in human populations | and evolution of language | extensive laboratory exercises in model-building and analyzing evolutionary data | extensive laboratory exercises in model-building and analyzing evolutionary data

License

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6.863J Natural Language and the Computer Representation of Knowledge (MIT) 6.863J Natural Language and the Computer Representation of Knowledge (MIT)

Description

6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design. 6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

Subjects

natural language processing | natural language processing | computational methods | computational methods | computer science | computer science | artificial intelligence | artificial intelligence | linguistic theory | linguistic theory | psycholinguistics | psycholinguistics | applications | applications | thematic structure | thematic structure | lexical-conceptual structure | lexical-conceptual structure | semantic structure | semantic structure | pragmatic structure | pragmatic structure | discourse structure | discourse structure | phonology | phonology | morphology | morphology | 2-level morphology | 2-level morphology | kimmo | kimmo | hmm tagging | hmm tagging | tagging | tagging | rule-based tagging | rule-based tagging | part of speech tagging | part of speech tagging | brill tagger | brill tagger | parsing | parsing | syntax | syntax | automata | automata | word modeling | word modeling | grammars | grammars | parsing algorithms | parsing algorithms | shift-reduce parsers | shift-reduce parsers | Earley's algorithm | Earley's algorithm | chart parsing | chart parsing | context-free parsing | context-free parsing | feature-based parsing | feature-based parsing | natural language system design | natural language system design | integrated lexicon | integrated lexicon | syntactic features | syntactic features | semantic interpretation | semantic interpretation | compositionality | compositionality | quantifiers | quantifiers | lexical semantic | lexical semantic | semantics | semantics | machine translation | machine translation | language learning | language learning | computational models of language | computational models of language | origins of language | origins of language | 6.863 | 6.863 | 9.611 | 9.611

License

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9.675 The Development of Object and Face Recognition (MIT) 9.675 The Development of Object and Face Recognition (MIT)

Description

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

Subjects

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

License

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9.66J Computational Cognitive Science (MIT) 9.66J Computational Cognitive Science (MIT)

Description

This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have? This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?

Subjects

computational theory | computational theory | human cognition | human cognition | artificial intelligence | artificial intelligence | human knowledge representation | human knowledge representation | inductive learning | inductive learning | inductive reasoning | inductive reasoning | innate knowledge | innate knowledge | machine learning | machine learning | cognitive science | cognitive science | computational cognitive science | computational cognitive science | 9.66 | 9.66 | 6.804 | 6.804

License

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16.100 Aerodynamics (MIT) 16.100 Aerodynamics (MIT)

Description

This course extends fluid mechanic concepts from Unified Engineering to the aerodynamic performance of wings and bodies in sub/supersonic regimes. 16.100 generally has four components: subsonic potential flows, including source/vortex panel methods; viscous flows, including laminar and turbulent boundary layers; aerodynamics of airfoils and wings, including thin airfoil theory, lifting line theory, and panel method/interacting boundary layer methods; and supersonic and hypersonic airfoil theory. Course material varies each year depending upon the focus of the design problem. This course extends fluid mechanic concepts from Unified Engineering to the aerodynamic performance of wings and bodies in sub/supersonic regimes. 16.100 generally has four components: subsonic potential flows, including source/vortex panel methods; viscous flows, including laminar and turbulent boundary layers; aerodynamics of airfoils and wings, including thin airfoil theory, lifting line theory, and panel method/interacting boundary layer methods; and supersonic and hypersonic airfoil theory. Course material varies each year depending upon the focus of the design problem.

Subjects

aerodynamics | aerodynamics | airflow | airflow | air | air | body | body | aircraft | aircraft | aerodynamic modes | aerodynamic modes | aero | aero | forces | forces | flow | flow | computational | computational | CFD | CFD | aerodynamic analysis | aerodynamic analysis | lift | lift | drag | drag | potential flows | potential flows | imcompressible | imcompressible | supersonic | supersonic | subsonic | subsonic | panel method | panel method | vortex lattice method | vortex lattice method | boudary layer | boudary layer | transition | transition | turbulence | turbulence | inviscid | inviscid | viscous | viscous | euler | euler | navier-stokes | navier-stokes | wind tunnel | wind tunnel | flow similarity | flow similarity | non-dimensional | non-dimensional | mach number | mach number | reynolds number | reynolds number | integral momentum | integral momentum | airfoil | airfoil | wing | wing | stall | stall | friction drag | friction drag | induced drag | induced drag | wave drag | wave drag | pressure drag | pressure drag | fluid element | fluid element | shear strain | shear strain | normal strain | normal strain | vorticity | vorticity | divergence | divergence | substantial derivative | substantial derivative | laminar | laminar | displacement thickness | displacement thickness | momentum thickness | momentum thickness | skin friction | skin friction | separation | separation | velocity profile | velocity profile | 2-d panel | 2-d panel | 3-d vortex | 3-d vortex | thin airfoil | thin airfoil | lifting line | lifting line | aspect ratio | aspect ratio | twist | twist | camber | camber | wing loading | wing loading | roll moments | roll moments | finite volume approximation | finite volume approximation | shocks | shocks | expansion fans | expansion fans | shock-expansion theory | shock-expansion theory | transonic | transonic | critical mach number | critical mach number | wing sweep | wing sweep | Kutta condition | Kutta condition | team project | team project | blended-wing-body | blended-wing-body | computational fluid dynamics | computational fluid dynamics

License

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MAS.110 Fundamentals of Computational Media Design (MIT) MAS.110 Fundamentals of Computational Media Design (MIT)

Description

This class covers the history of 20th century art and design from the perspective of the technologist. Methods for visual analysis, oral critique, and digital expression are introduced. Class projects this term use the OLPC XO (One Laptop Per Child) laptop, Csound and Python software. This class covers the history of 20th century art and design from the perspective of the technologist. Methods for visual analysis, oral critique, and digital expression are introduced. Class projects this term use the OLPC XO (One Laptop Per Child) laptop, Csound and Python software.

Subjects

analysis | analysis | synthesis | synthesis | computational media | computational media | computational and traditional arts | computational and traditional arts | typography | typography | design | design | programming | programming | javascript | javascript | contemporary digital art | contemporary digital art | machine age | machine age | media design | media design | analog vs digital art | analog vs digital art | graphic design | graphic design | web design | web design | photography | photography | storytelling | storytelling | modern art | modern art | internet design | internet design | web 2.0 | web 2.0 | XO laptop | XO laptop | OLPC | OLPC

License

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 7.91 | 20.490 | 20.490 | 20.390 | 20.390 | 7.36 | 7.36 | 6.802 | 6.802 | 6.874 | 6.874 | HST.506 | HST.506 | computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | artificial intelligence | artificial intelligence | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation

License

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation | BE.490J | BE.490J | 7.91 | 7.91 | 7.36 | 7.36 | BE.490 | BE.490 | 20.490 | 20.490

License

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6.891 Computational Evolutionary Biology (MIT) 6.891 Computational Evolutionary Biology (MIT)

Description

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data. Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

Subjects

evolution from a computational | evolution from a computational | modeling | modeling | and engineering perspective | and engineering perspective | analyzing evolutionary data | analyzing evolutionary data | vaccine | vaccine | polio | polio | influenza | influenza | AIDS | AIDS | evolutionary extinction | evolutionary extinction | sex | sex | parasites | parasites | modern genomics | modern genomics | polio vaccine | polio vaccine | hands-on | hands-on | evolution from a computational | modeling | and engineering perspective | evolution from a computational | modeling | and engineering perspective

License

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6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.

Subjects

Randomized Algorithms | algorithms | efficient in time and space | randomization | computational problems | data structures | graph algorithms | optimization | geometry | Markov chains | sampling | estimation | geometric algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and distributed algorithm | random sampling | random selection of witnesses | symmetry breaking | randomized computational models | hash tables | skip lists | minimum spanning trees | shortest paths | minimum cuts | convex hulls | linear programming | fixed dimension | arbitrary dimension | approximate counting | parallel algorithms | online algorithms | derandomization techniques | probabilistic analysis | computational number theory | simplicity | speed | design | basic probability theory | application | randomized complexity classes | game-theoretic techniques | Chebyshev | moment inequalities | limited independence | coupon collection | occupancy problems | tail inequalities | Chernoff bound | conditional expectation | probabilistic method | random walks | algebraic techniques | probability amplification | sorting | searching | combinatorial optimization | approximation | counting problems | distributed algorithms | 6.856 | 18.416

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6.172 Performance Engineering of Software Systems (MIT) 6.172 Performance Engineering of Software Systems (MIT)

Description

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 pr

Subjects

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 | render

License

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3.021J Introduction to Modeling and Simulation (MIT) 3.021J Introduction to Modeling and Simulation (MIT)

Description

This course explores the basic concepts of computer modeling and simulation in science and engineering. We'll use techniques and software for simulation, data analysis and visualization. Continuum, mesoscale, atomistic and quantum methods are used to study fundamental and applied problems in physics, chemistry, materials science, mechanics, engineering, and biology. Examples drawn from the disciplines above are used to understand or characterize complex structures and materials, and complement experimental observations. This course explores the basic concepts of computer modeling and simulation in science and engineering. We'll use techniques and software for simulation, data analysis and visualization. Continuum, mesoscale, atomistic and quantum methods are used to study fundamental and applied problems in physics, chemistry, materials science, mechanics, engineering, and biology. Examples drawn from the disciplines above are used to understand or characterize complex structures and materials, and complement experimental observations.

Subjects

computer modeling | computer modeling | discrete particle system | discrete particle system | continuum | continuum | continuum field | continuum field | statistical sampling | statistical sampling | data analysis | data analysis | visualization | visualization | quantum | quantum | quantum method | quantum method | chemical | chemical | molecular dynamics | molecular dynamics | Monte Carlo | Monte Carlo | mesoscale | mesoscale | continuum method | continuum method | computational physics | computational physics | chemistry | chemistry | mechanics | mechanics | materials science | materials science | biology | biology | applied mathematics | applied mathematics | fluid dynamics | fluid dynamics | heat | heat | fractal | fractal | evolution | evolution | melting | melting | gas | gas | structural mechanics | structural mechanics | FEM | FEM | finite element | finite element

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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

License

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6.253 Convex Analysis and Optimization (MIT) 6.253 Convex Analysis and Optimization (MIT)

Description

6.253 develops the core analytical issues of continuous optimization, duality, and saddle point theory, using a handful of unifying principles that can be easily visualized and readily understood. The mathematical theory of convex sets and functions is discussed in detail, and is the basis for an intuitive, highly visual, geometrical approach to the subject. 6.253 develops the core analytical issues of continuous optimization, duality, and saddle point theory, using a handful of unifying principles that can be easily visualized and readily understood. The mathematical theory of convex sets and functions is discussed in detail, and is the basis for an intuitive, highly visual, geometrical approach to the subject.

Subjects

affine hulls | affine hulls | recession cones | recession cones | global minima | global minima | local minima | local minima | optimal solutions | optimal solutions | hyper planes | hyper planes | minimax theory | minimax theory | polyhedral convexity | polyhedral convexity | polyhedral cones | polyhedral cones | polyhedral sets | polyhedral sets | convex analysis | convex analysis | optimization | optimization | convexity | convexity | Lagrange multipliers | Lagrange multipliers | duality | duality | continuous optimization | continuous optimization | saddle point theory | saddle point theory | linear algebra | linear algebra | real analysis | real analysis | convex sets | convex sets | convex functions | convex functions | extreme points | extreme points | subgradients | subgradients | constrained optimization | constrained optimization | directional derivatives | directional derivatives | subdifferentials | subdifferentials | conical approximations | conical approximations | Fritz John optimality | Fritz John optimality | Exact penalty functions | Exact penalty functions | conjugate duality | conjugate duality | conjugate functions | conjugate functions | Fenchel duality | Fenchel duality | exact penalty functions | exact penalty functions | dual computational methods | dual computational methods

License

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7.89 Topics in Computational and Systems Biology (MIT) 7.89 Topics in Computational and Systems Biology (MIT)

Description

This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB PhD program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology. Acknowledgments In addition to the staff listed on this page, the followi This is a seminar based on research literature. Papers covered are selected to illustrate important problems and approaches in the field of computational and systems biology, and provide students a framework from which to evaluate new developments. The MIT Initiative in Computational and Systems Biology (CSBi) is a campus-wide research and education program that links biology, engineering, and computer science in a multidisciplinary approach to the systematic analysis and modeling of complex biological phenomena. This course is one of a series of core subjects offered through the CSB PhD program, for students with an interest in interdisciplinary training and research in the area of computational and systems biology. Acknowledgments In addition to the staff listed on this page, the followi

Subjects

computational | computational | systems | systems | biology | biology | seminar | seminar | literature review | literature review | statistics | statistics | developmental | developmental | biochemistry | biochemistry | genetics | genetics | physics | physics | genomics | genomics | signal transduction | signal transduction | regulation | regulation | medicine | medicine | kinetics | kinetics | protein structure | protein structure | devices | devices | synthesis | synthesis | networks | networks | mapping | mapping

License

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HST.950J Engineering Biomedical Information: From Bioinformatics to Biosurveillance (MIT) HST.950J Engineering Biomedical Information: From Bioinformatics to Biosurveillance (MIT)

Description

This course provides an interdisciplinary introduction to the technological advances in biomedical informatics and their applications at the intersection of computer science and biomedical research. This course provides an interdisciplinary introduction to the technological advances in biomedical informatics and their applications at the intersection of computer science and biomedical research.

Subjects

biomedical informatics | biomedical informatics | bioinformatics | bioinformatics | biomedical research | biomedical research | biological computing | biological computing | biomedical computing | biomedical computing | computational genomics | computational genomics | genomics | genomics | microarrays | microarrays | proteomics | proteomics | pharmacogenomics | pharmacogenomics | genomic privacy | genomic privacy | clinical informatics | clinical informatics | biosurveillance | biosurveillance | privacy | privacy | biotechnology | biotechnology

License

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12.804 Large-scale Flow Dynamics Lab (MIT) 12.804 Large-scale Flow Dynamics Lab (MIT)

Description

12.804 is a laboratory accompaniment to 12.803, Quasi-balanced Circulations in Oceans and Atmospheres. The subject includes analysis of observations of oceanic and atmospheric quasi-balanced flows, computational models, and rotating tank experiments. Student projects illustrate the basic principles of potential vorticity conservation and inversion, Rossby wave propagation, baroclinic instability, and the behavior of isolated vortices. 12.804 is a laboratory accompaniment to 12.803, Quasi-balanced Circulations in Oceans and Atmospheres. The subject includes analysis of observations of oceanic and atmospheric quasi-balanced flows, computational models, and rotating tank experiments. Student projects illustrate the basic principles of potential vorticity conservation and inversion, Rossby wave propagation, baroclinic instability, and the behavior of isolated vortices.

Subjects

flow dynamics laboratory | flow dynamics laboratory | oceanic | oceanic | atmospheric | atmospheric | quasi-balanced flows | quasi-balanced flows | computational models | computational models | rotating tank experiments | rotating tank experiments | potential vorticity conservation | potential vorticity conservation | potential vorticity inversion | potential vorticity inversion | Rossby waves | Rossby waves | Rossby wave propagation | Rossby wave propagation | baroclinic instability | baroclinic instability | vortices | vortices

License

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6.852J Distributed Algorithms (MIT) 6.852J Distributed Algorithms (MIT)

Description

This course intends to provide a rigorous introduction to the most important research results in the area of distributed algorithms, and prepare interested students to carry out independent research in distributed algorithms. Topics covered include: design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks, process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration is given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation are also discussed. Detailed information on the This course intends to provide a rigorous introduction to the most important research results in the area of distributed algorithms, and prepare interested students to carry out independent research in distributed algorithms. Topics covered include: design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks, process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration is given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation are also discussed. Detailed information on the

Subjects

distributed algorithms | distributed algorithms | algorithm | algorithm | concurrent algorithms | concurrent algorithms | distributed networks | distributed networks | process synchronization | process synchronization | computational resources | computational resources | distributed consensus | distributed consensus | distributed graph algorithms | distributed graph algorithms | distributed termination | distributed termination | deadlock detection | deadlock detection | concurrency control | concurrency control | communication | communication | clock synchronization | clock synchronization | fault tolerance | fault tolerance | distributed computation | distributed computation | 6.852 | 6.852 | 18.437 | 18.437

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HST.725 Music Perception and Cognition (MIT) HST.725 Music Perception and Cognition (MIT)

Description

Survey of perceptual and cognitive aspects of the psychology of music, with special emphasis on underlying neuronal and neurocomputational representations and mechanisms. Basic perceptual dimensions of hearing (pitch, timbre, consonance/roughness, loudness, auditory grouping) form salient qualities, contrasts, patterns and streams that are used in music to convey melody, harmony, rhythm and separate voices. Perceptual, cognitive, and neurophysiological aspects of the temporal dimension of music (rhythm, timing, duration, temporal expectation) are explored. Special topics include comparative, evolutionary, and developmental psychology of music perception, biological vs. cultural influences, Gestaltist vs. associationist vs. schema-based theories, comparison of music and speech perception, p Survey of perceptual and cognitive aspects of the psychology of music, with special emphasis on underlying neuronal and neurocomputational representations and mechanisms. Basic perceptual dimensions of hearing (pitch, timbre, consonance/roughness, loudness, auditory grouping) form salient qualities, contrasts, patterns and streams that are used in music to convey melody, harmony, rhythm and separate voices. Perceptual, cognitive, and neurophysiological aspects of the temporal dimension of music (rhythm, timing, duration, temporal expectation) are explored. Special topics include comparative, evolutionary, and developmental psychology of music perception, biological vs. cultural influences, Gestaltist vs. associationist vs. schema-based theories, comparison of music and speech perception, p

Subjects

music perception | music perception | music cognition | music cognition | music memory | music memory | pitch | pitch | timbre | timbre | consonance | consonance | harmony | harmony | tonality | tonality | melody | melody | expressive timing | expressive timing | rhythmic hierarchies | rhythmic hierarchies | auditory perception | auditory perception | auditory pathway | auditory pathway | musical acoustics | musical acoustics | power spectra | power spectra | psychophysics | psychophysics | neurocomputational models | neurocomputational models | neural correlates | neural correlates | music therapy | music therapy | synesthesia | synesthesia | absolute pitch | absolute pitch

License

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11.127 Computer Games and Simulations for Investigation and Education (MIT) 11.127 Computer Games and Simulations for Investigation and Education (MIT)

Description

This course will explore educational games and simulations and several computer modeling platforms. We will focus on design and research issues pertinent to learning through simulations and games. Throughout the course we will explore concepts in modeling, simulation, and gaming common to many domains, and investigate specific applications from a variety of fields ranging from weather to ecology to traffic management. This course will explore educational games and simulations and several computer modeling platforms. We will focus on design and research issues pertinent to learning through simulations and games. Throughout the course we will explore concepts in modeling, simulation, and gaming common to many domains, and investigate specific applications from a variety of fields ranging from weather to ecology to traffic management.

Subjects

simulation modeling | simulation modeling | computational technology | computational technology | SimCity | SimCity | edutainment | edutainment | "edutainment" software | "edutainment" software | Civilization | Civilization | pre-built models | pre-built models | gaming | gaming | game creation | game creation | game theory | game theory | design | design | simulation creation | simulation creation | software | software | programming | programming

License

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22.00J Introduction to Modeling and Simulation (MIT) 22.00J Introduction to Modeling and Simulation (MIT)

Description

This course surveys the basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. It covers techniques and software for statistical sampling, simulation, data analysis and visualization, and uses statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications are drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal footing with theory and experiment. A term project allows development of individual interests. Students are mentor This course surveys the basic concepts of computer modeling in science and engineering using discrete particle systems and continuum fields. It covers techniques and software for statistical sampling, simulation, data analysis and visualization, and uses statistical, quantum chemical, molecular dynamics, Monte Carlo, mesoscale and continuum methods to study fundamental physical phenomena encountered in the fields of computational physics, chemistry, mechanics, materials science, biology, and applied mathematics. Applications are drawn from a range of disciplines to build a broad-based understanding of complex structures and interactions in problems where simulation is on equal footing with theory and experiment. A term project allows development of individual interests. Students are mentor

Subjects

computer modeling | computer modeling | discrete particle system | discrete particle system | continuum | continuum | continuum field | continuum field | statistical sampling | statistical sampling | data analysis | data analysis | visualization | visualization | quantum | quantum | quantum method | quantum method | chemical | chemical | molecular dynamics | molecular dynamics | Monte Carlo | Monte Carlo | mesoscale | mesoscale | continuum method | continuum method | computational physics | computational physics | chemistry | chemistry | mechanics | mechanics | materials science | materials science | biology; applied mathematics | biology; applied mathematics | fluid dynamics | fluid dynamics | heat | heat | fractal | fractal | evolution | evolution | melting | melting | gas | gas | structural mechanics | structural mechanics | FEM | FEM | finite element | finite element | biology | biology | applied mathematics | applied mathematics | 1.021 | 1.021 | 2.030 | 2.030 | 3.021 | 3.021 | 10.333 | 10.333 | 18.361 | 18.361 | HST.588 | HST.588 | 22.00 | 22.00

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6.00 Introduction to Computer Science and Programming (MIT) 6.00 Introduction to Computer Science and Programming (MIT)

Description

This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python™ programming language. This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python™ programming language.

Subjects

computer science | computer science | computation | computation | problem solving | problem solving | Python programming | Python programming | recursion | recursion | binary search | binary search | classes | classes | inheritance | inheritance | libraries | libraries | algorithms | algorithms | optimization problems | optimization problems | modules | modules | simulation | simulation | big O notation | big O notation | control flow | control flow | exceptions | exceptions | building computational models | building computational models | software engineering | software engineering

License

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