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12.990 Prediction and Predictability in the Atmosphere and Oceans (MIT) 12.990 Prediction and Predictability in the Atmosphere and Oceans (MIT)

Description

Forecasting is the ultimate form of model validation. But even if a perfect model is in hand, imperfect forecasts are likely. This course will cover the factors that limit our ability to produce good forecasts, will show how the quality of forecasts can be gauged a priori (predicting our ability to predict!), and will cover the state of the art in operational atmosphere and ocean forecasting systems. Forecasting is the ultimate form of model validation. But even if a perfect model is in hand, imperfect forecasts are likely. This course will cover the factors that limit our ability to produce good forecasts, will show how the quality of forecasts can be gauged a priori (predicting our ability to predict!), and will cover the state of the art in operational atmosphere and ocean forecasting systems.

Subjects

Forecasting | Forecasting | model validation | model validation | prediction quality | prediction quality | operational atmosphere and ocean forecasting systems | operational atmosphere and ocean forecasting systems | limiting factors | limiting factors | prediction | prediction | operational atmosphere forecasting systems | operational atmosphere forecasting systems | ocean forecasting systems | ocean forecasting systems | chaos | chaos | probabilistic forecasting | probabilistic forecasting | data assimilation | data assimilation | adaptive observations | adaptive observations | model error | model error | attractors | attractors | dimensions | dimensions | sensitive dependence | sensitive dependence | initial conditions | initial conditions

License

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.htm

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12.006J Nonlinear Dynamics I: Chaos (MIT) 12.006J Nonlinear Dynamics I: Chaos (MIT)

Description

This course provides an introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. The content is structured to be of general interest to undergraduates in science and engineering. This course provides an introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. The content is structured to be of general interest to undergraduates in science and engineering.

Subjects

Forced and parametric oscillators | Forced and parametric oscillators | Phase space | Phase space | Periodic | quasiperiodic | and aperiodic flows | Periodic | quasiperiodic | and aperiodic flows | Sensitivity to initial conditions and strange attractors | Sensitivity to initial conditions and strange attractors | Lorenz attractor | Lorenz attractor | Period doubling | intermittency | and quasiperiodicity | Period doubling | intermittency | and quasiperiodicity | Scaling and universality | Scaling and universality | Analysis of experimental data: Fourier transforms | Analysis of experimental data: Fourier transforms | Poincar? sections | Poincar? sections | fractal dimension | fractal dimension | Lyaponov exponents | Lyaponov exponents

License

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.htm

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9.641J Introduction to Neural Networks (MIT) 9.641J Introduction to Neural Networks (MIT)

Description

This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development. This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.

Subjects

synaptic connectivity | synaptic connectivity | computation | computation | learning | learning | multilayer perceptrons | multilayer perceptrons | recurrent networks | recurrent networks | amplifiers | amplifiers | attractors | attractors | hybrid computation | hybrid computation | Backpropagation | Backpropagation | Hebbian learning | Hebbian learning | perception | perception | motor control | motor control | memory | memory | neural development | neural development

License

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.htm

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12.006J Nonlinear Dynamics I: Chaos (MIT) 12.006J Nonlinear Dynamics I: Chaos (MIT)

Description

This course provides an introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. The content is structured to be of general interest to undergraduates in science and engineering. This course provides an introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. The content is structured to be of general interest to undergraduates in science and engineering.

Subjects

Forced and parametric oscillators | Forced and parametric oscillators | Phase space | Phase space | Periodic | quasiperiodic | and aperiodic flows | Periodic | quasiperiodic | and aperiodic flows | Sensitivity to initial conditions and strange attractors | Sensitivity to initial conditions and strange attractors | Lorenz attractor | Lorenz attractor | Period doubling | intermittency | and quasiperiodicity | Period doubling | intermittency | and quasiperiodicity | Scaling and universality | Scaling and universality | Analysis of experimental data: Fourier transforms | Analysis of experimental data: Fourier transforms | Poincar? sections | Poincar? sections | fractal dimension | fractal dimension | Lyaponov exponents | Lyaponov exponents

License

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.htm

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9.641J Introduction to Neural Networks (MIT) 9.641J Introduction to Neural Networks (MIT)

Description

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development. Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development.

Subjects

synaptic connectivity | synaptic connectivity | computation | computation | learning | learning | multilayer perceptrons | multilayer perceptrons | recurrent networks | recurrent networks | amplifiers | amplifiers | attractors | attractors | hybrid computation | hybrid computation | Backpropagation | Backpropagation | Hebbian learning | Hebbian learning | perception | perception | motor control | motor control | memory | memory | neural development | neural development | 9.641 | 9.641 | 8.594 | 8.594

License

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.htm

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9.641J Introduction to Neural Networks (MIT)

Description

This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.

Subjects

synaptic connectivity | computation | learning | multilayer perceptrons | recurrent networks | amplifiers | attractors | hybrid computation | Backpropagation | Hebbian learning | perception | motor control | memory | neural development

License

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

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9.641J Introduction to Neural Networks (MIT)

Description

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development.

Subjects

synaptic connectivity | computation | learning | multilayer perceptrons | recurrent networks | amplifiers | attractors | hybrid computation | Backpropagation | Hebbian learning | perception | motor control | memory | neural development | 9.641 | 8.594

License

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

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Attribution

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9.641J Introduction to Neural Networks (MIT)

Description

This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.

Subjects

synaptic connectivity | computation | learning | multilayer perceptrons | recurrent networks | amplifiers | attractors | hybrid computation | Backpropagation | Hebbian learning | perception | motor control | memory | neural development

License

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

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12.990 Prediction and Predictability in the Atmosphere and Oceans (MIT)

Description

Forecasting is the ultimate form of model validation. But even if a perfect model is in hand, imperfect forecasts are likely. This course will cover the factors that limit our ability to produce good forecasts, will show how the quality of forecasts can be gauged a priori (predicting our ability to predict!), and will cover the state of the art in operational atmosphere and ocean forecasting systems.

Subjects

Forecasting | model validation | prediction quality | operational atmosphere and ocean forecasting systems | limiting factors | prediction | operational atmosphere forecasting systems | ocean forecasting systems | chaos | probabilistic forecasting | data assimilation | adaptive observations | model error | attractors | dimensions | sensitive dependence | initial conditions

License

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

Site sourced from

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

Attribution

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12.006J Nonlinear Dynamics I: Chaos (MIT)

Description

This course provides an introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. The content is structured to be of general interest to undergraduates in science and engineering.

Subjects

Forced and parametric oscillators | Phase space | Periodic | quasiperiodic | and aperiodic flows | Sensitivity to initial conditions and strange attractors | Lorenz attractor | Period doubling | intermittency | and quasiperiodicity | Scaling and universality | Analysis of experimental data: Fourier transforms | Poincar? sections | fractal dimension | Lyaponov exponents

License

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

Site sourced from

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

Attribution

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12.006J Nonlinear Dynamics I: Chaos (MIT)

Description

This course provides an introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. The content is structured to be of general interest to undergraduates in science and engineering.

Subjects

Forced and parametric oscillators | Phase space | Periodic | quasiperiodic | and aperiodic flows | Sensitivity to initial conditions and strange attractors | Lorenz attractor | Period doubling | intermittency | and quasiperiodicity | Scaling and universality | Analysis of experimental data: Fourier transforms | Poincar? sections | fractal dimension | Lyaponov exponents

License

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

Site sourced from

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

Attribution

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9.641J Introduction to Neural Networks (MIT)

Description

This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.

Subjects

synaptic connectivity | computation | learning | multilayer perceptrons | recurrent networks | amplifiers | attractors | hybrid computation | Backpropagation | Hebbian learning | perception | motor control | memory | neural development

License

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

Site sourced from

https://ocw.mit.edu/rss/all/mit-allspanishcourses.xml

Attribution

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12.006J Nonlinear Dynamics I: Chaos (MIT)

Description

This course provides an introduction to the theory and phenomenology of nonlinear dynamics and chaos in dissipative systems. The content is structured to be of general interest to undergraduates in science and engineering.

Subjects

Forced and parametric oscillators | Phase space | Periodic | quasiperiodic | and aperiodic flows | Sensitivity to initial conditions and strange attractors | Lorenz attractor | Period doubling | intermittency | and quasiperiodicity | Scaling and universality | Analysis of experimental data: Fourier transforms | Poincar? sections | fractal dimension | Lyaponov exponents

License

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.htm

Site sourced from

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

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