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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 conditionsLicense

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 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 annotationLicense

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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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 | 20.490License

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

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See all metadata14.384 Time Series Analysis (MIT) 14.384 Time Series Analysis (MIT)

Description

Subjects

univariate stationary | univariate stationary | univariate non-stationary | univariate non-stationary | vector autoregressions | vector autoregressions | frequency domain analysis | frequency domain analysis | persistent time series | persistent time series | structural breaks | structural breaks | dynamic stochastic general equilibrium | dynamic stochastic general equilibrium | DSGE | DSGE | Bayesian | Bayesian | econometrics | econometrics | VAR | VAR | unit root | unit root | prediction regression | prediction regression | GMM | GMM | MCMC | MCMCLicense

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 addresses the challenges of defining a relationship between exposure to environmental chemicals and human disease. Course topics include epidemiological approaches to understanding disease causation; biostatistical methods; evaluation of human exposure to chemicals, and their internal distribution, metabolism, reactions with cellular components, and biological effects; and qualitative and quantitative health risk assessment methods used in the U.S. as bases for regulatory decision-making. Throughout the term, students consider case studies of local and national interest. This course addresses the challenges of defining a relationship between exposure to environmental chemicals and human disease. Course topics include epidemiological approaches to understanding disease causation; biostatistical methods; evaluation of human exposure to chemicals, and their internal distribution, metabolism, reactions with cellular components, and biological effects; and qualitative and quantitative health risk assessment methods used in the U.S. as bases for regulatory decision-making. Throughout the term, students consider case studies of local and national interest.Subjects

biostatistics | biostatistics | risk | risk | risk analysis | risk analysis | risk factor | risk factor | environmental agent | environmental agent | environetics | environetics | cause and effect | cause and effect | pollution | pollution | statistical analysis | statistical analysis | toxic | toxic | genetics | genetics | disease | disease | health | health | EPA | EPA | metabolism | metabolism | endocrine | endocrine | immunity | immunity | uncertainty | uncertainty | mortality | mortality | death rate | death rate | prediction | predictionLicense

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 metadataWhy is climate change so difficult to understand?

Description

The second lecture of a series entitled 'Climate Connections' was presented by Carl Wunsch, Cecil and Ida Green Professor of Physical Oceanography at the Massachusetts Institute of Technology. In this talk, Prof Wunsch considers the perhaps unique problems faced by scientists and the public in understanding climate change. The problems include the very long times over which climate can and does change--far longer than human working lifetimes; the intuitive belief that the world is deterministic with randomness unimportant; the corresponding tendency to see patterns where none exist; the insistence by governments that scientists must tell them what will happen in the future; the liking of many scientists for the media limelight; the widespread confusion between weather and climate; the ri Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/Subjects

climate change | global warming | weather prediction | climate change | global warming | weather predictionLicense

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This course provides a review of physical, chemical, ecological, and economic principles used to examine interactions between humans and the natural environment. Mass balance concepts are applied to ecology, chemical kinetics, hydrology, and transportation; energy balance concepts are applied to building design, ecology, and climate change; and economic and life cycle concepts are applied to resource evaluation and engineering design. Numerical models are used to integrate concepts and to assess environmental impacts of human activities. Problem sets involve development of MATLAB® models for particular engineering applications. Some experience with computer programming is helpful but not essential. This course provides a review of physical, chemical, ecological, and economic principles used to examine interactions between humans and the natural environment. Mass balance concepts are applied to ecology, chemical kinetics, hydrology, and transportation; energy balance concepts are applied to building design, ecology, and climate change; and economic and life cycle concepts are applied to resource evaluation and engineering design. Numerical models are used to integrate concepts and to assess environmental impacts of human activities. Problem sets involve development of MATLAB® models for particular engineering applications. Some experience with computer programming is helpful but not essential.Subjects

modeling | modeling | matlab | matlab | human impact on environment | human impact on environment | economics | economics | natural resources | natural resources | assessment of model predictions | assessment of model predictions | mass balance | mass balance | energy balance | energy balance | mass transport | mass transport | energy transport | energy transport | resource economics | resource economics | life cycle analysis | life cycle analysis | chemical kinetics | chemical kinetics | population modeling | population modeling | pesticides | pesticides | nutrients | nutrients | building energy | building energy | air quality | air quality | crop irrigation | crop irrigation | groundwater | groundwaterLicense

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 metadata5.68J Kinetics of Chemical Reactions (MIT) 5.68J Kinetics of Chemical Reactions (MIT)

Description

This course deals with the experimental and theoretical aspects of chemical reaction kinetics, including transition-state theories, molecular beam scattering, classical techniques, quantum and statistical mechanical estimation of rate constants, pressure-dependence and chemical activation, modeling complex reacting mixtures, and uncertainty/sensitivity analyses. Reactions in the gas phase, liquid phase, and on surfaces are discussed with examples drawn from atmospheric, combustion, industrial, catalytic, and biological chemistry. This course deals with the experimental and theoretical aspects of chemical reaction kinetics, including transition-state theories, molecular beam scattering, classical techniques, quantum and statistical mechanical estimation of rate constants, pressure-dependence and chemical activation, modeling complex reacting mixtures, and uncertainty/sensitivity analyses. Reactions in the gas phase, liquid phase, and on surfaces are discussed with examples drawn from atmospheric, combustion, industrial, catalytic, and biological chemistry.Subjects

quantum mechanics | quantum mechanics | statistical mechanics | statistical mechanics | chemical reaction kinetics | chemical reaction kinetics | transition-state theories | transition-state theories | molecular beam scattering | molecular beam scattering | classical techniques | classical techniques | rate constants | rate constants | pressure-dependence | pressure-dependence | chemical activation | chemical activation | atmosphere | atmosphere | combustion | combustion | catalytic | catalytic | biological chemistry | biological chemistry | elementary kinetics | elementary kinetics | experimental kinetics | experimental kinetics | reaction rate theory | reaction rate theory | thermodynamics | thermodynamics | practical prediction methods | practical prediction methods | handling large kinetic models | handling large kinetic models | reactions in solution | reactions in solution | catalysis | catalysis | 5.68 | 5.68 | 10.652 | 10.652License

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See all metadata6.341 Discrete-Time Signal Processing (MIT) 6.341 Discrete-Time Signal Processing (MIT)

Description

This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications. Acknowledgements I would like to express my thanks to Thomas Baran, Myung Jin Choi, and Xiaomeng Shi for compiling the lecture notes on this site from my individual lectures and handouts and their class notes during the semest This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing of continuous-time signals; decimation, interpolation, and sampling rate conversion; flowgraph structures for DT systems; time-and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT algorithm; short-time Fourier analysis and filter banks; multirate techniques; Hilbert transforms; Cepstral analysis and various applications. Acknowledgements I would like to express my thanks to Thomas Baran, Myung Jin Choi, and Xiaomeng Shi for compiling the lecture notes on this site from my individual lectures and handouts and their class notes during the semestSubjects

discrete time signals and systems | discrete time signals and systems | discrete-time processing of continuous-time signals | discrete-time processing of continuous-time signals | decimation | decimation | interpolation | interpolation | sampling rate conversion | sampling rate conversion | Flowgraph structures | Flowgraph structures | time- and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters | time- and frequency-domain design techniques for recursive (IIR) and non-recursive (FIR) filters | linear prediction | linear prediction | Discrete Fourier transform | Discrete Fourier transform | FFT algorithm | FFT algorithm | Short-time Fourier analysis and filter banks | Short-time Fourier analysis and filter banks | Multirate techniques | Multirate techniques | Hilbert transforms | Hilbert transforms | Cepstral analysis | Cepstral analysisLicense

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 metadata6.435 System Identification (MIT) 6.435 System Identification (MIT)

Description

This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues. This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.Subjects

mathematical models | mathematical models | time series | time series | state-space | state-space | input-output models | input-output models | model structures | model structures | parametrization | parametrization | identifiability | identifiability | non-parametric methods | non-parametric methods | prediction error | prediction error | parameter estimation | parameter estimation | convergence | convergence | consistency | consistency | andasymptotic distribution | andasymptotic distribution | maximum likelihood estimation | maximum likelihood estimation | recursive estimation | recursive estimation | Kalman filters | Kalman filters | structure determination | structure determination | order estimation | order estimation | Akaike criterion | Akaike criterion | bounded noise models | bounded noise models | robustness | robustnessLicense

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 examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions; and detection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters. This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions; and detection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters.Subjects

stochastic process | stochastic process | detection | detection | estimation | estimation | signal processing | signal processing | communications | communications | control | control | vector spaces | vector spaces | Bayesian | Bayesian | Neyman-Pearson | Neyman-Pearson | minimum-variance unbiased estimator | minimum-variance unbiased estimator | Cramer-Rao bounds | Cramer-Rao bounds | shaping filter | shaping filter | whitening filter | whitening filter | Karhunen-Loeve expansion | Karhunen-Loeve expansion | waveform observation | waveform observation | linear prediction | linear prediction | spectral estimation | spectral estimation | Wiener filter | Wiener filter | Kalman filter | Kalman filterLicense

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 metadata7.91J 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.Subjects

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

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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Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects. Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.Subjects

prediction | prediction | machine learning | machine learning | statistics | statistics | data mining | data mining | algorithms | algorithms | statistical learning theory | statistical learning theory | Bayesian analysis | Bayesian analysis | top 10 algorithms | top 10 algorithms | support vector machines | support vector machines | boosting | boostingLicense

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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Diversity begets creativity—in this seminar we tap the amazing power of swarm creativity on the Web by studying and working together as Collaborative Innovation Networks (COINs). As interdisciplinary teams of MIT management, SCAD design, University of Cologne informatics, and Aalto University software engineering students we will explore how to discover latest trends on the Web, and how to make them succeed in online social networks. We study a wide range of methods for predictive analytics (coolhunting) and online social marketing (coolfarming), mostly based on social network analysis and the emerging science of collaboration. Students will also learn to use our own unique MIT-developed Condor tool for Web mining, social network analysis, and trend prediction. Diversity begets creativity—in this seminar we tap the amazing power of swarm creativity on the Web by studying and working together as Collaborative Innovation Networks (COINs). As interdisciplinary teams of MIT management, SCAD design, University of Cologne informatics, and Aalto University software engineering students we will explore how to discover latest trends on the Web, and how to make them succeed in online social networks. We study a wide range of methods for predictive analytics (coolhunting) and online social marketing (coolfarming), mostly based on social network analysis and the emerging science of collaboration. Students will also learn to use our own unique MIT-developed Condor tool for Web mining, social network analysis, and trend prediction.Subjects

collaborative innovation networks | collaborative innovation networks | social networks | social networks | social marketing | social marketing | Web | Web | swarm creativity | swarm creativity | predictive analytics | predictive analytics | Web trends | Web trends | Facebook | Facebook | email | email | Web mining | Web mining | social network analysis | social network analysis | trend predictions | trend predictions | viral marketing | viral marketing | global virtual collaboration | global virtual collaborationLicense

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

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See all metadata16.512 Rocket Propulsion (MIT) 16.512 Rocket Propulsion (MIT)

Description

This class focuses on chemical rocket propulsion systems for launch, orbital, and interplanetary flight. It studies the modeling of solid, liquid-bipropellant, and hybrid rocket engines. Thermochemistry, prediction of specific impulse, and nozzle flows including real gas and kinetic effects will also be covered. Other topics to be covered include structural constraints, propellant feed systems, turbopumps, and combustion processes in solid, liquid, and hybrid rockets. This class focuses on chemical rocket propulsion systems for launch, orbital, and interplanetary flight. It studies the modeling of solid, liquid-bipropellant, and hybrid rocket engines. Thermochemistry, prediction of specific impulse, and nozzle flows including real gas and kinetic effects will also be covered. Other topics to be covered include structural constraints, propellant feed systems, turbopumps, and combustion processes in solid, liquid, and hybrid rockets.Subjects

chemical rocket propulsion systems for launch | chemical rocket propulsion systems for launch | orbital | orbital | and interplanetary flight | and interplanetary flight | Modeling of solid propellant | Modeling of solid propellant | liquid-bipropellant | liquid-bipropellant | hybrid rocket engines | hybrid rocket engines | thermochemistry | thermochemistry | prediction of specific impulse | prediction of specific impulse | nozzle flows including real gas and kinetic effects | nozzle flows including real gas and kinetic effects | structural constraints | structural constraints | propellant feed systems | propellant feed systems | turbopumps | turbopumps | combustion processes in solid | combustion processes in solid | liquid | liquid | and hybrid rockets | and hybrid rockets | cooling | cooling | heat sink | heat sink | ablative | ablativeLicense

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This course addresses the challenges of defining a relationship between exposure to environmental chemicals and human disease. Course topics include epidemiological approaches to understanding disease causation; biostatistical methods; evaluation of human exposure to chemicals, and their internal distribution, metabolism, reactions with cellular components, and biological effects; and qualitative and quantitative health risk assessment methods used in the U.S. as bases for regulatory decision-making. Throughout the term, students consider case studies of local and national interest. This course addresses the challenges of defining a relationship between exposure to environmental chemicals and human disease. Course topics include epidemiological approaches to understanding disease causation; biostatistical methods; evaluation of human exposure to chemicals, and their internal distribution, metabolism, reactions with cellular components, and biological effects; and qualitative and quantitative health risk assessment methods used in the U.S. as bases for regulatory decision-making. Throughout the term, students consider case studies of local and national interest.Subjects

biostatistics | biostatistics | risk | risk | risk analysis | risk analysis | risk factor | risk factor | environmental agent | environmental agent | environetics | environetics | cause and effect | cause and effect | pollution | pollution | statistical analysis | statistical analysis | toxic | toxic | genetics | genetics | disease | disease | health | health | EPA | EPA | metabolism | metabolism | endocrine | endocrine | immunity | immunity | uncertainty | uncertainty | mortality | mortality | death rate | death rate | prediction | prediction | 20.104 | 20.104 | 1.081 | 1.081 | ESD.053 | ESD.053 | BE.104J | BE.104J | BE.104 | BE.104License

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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"The Art of the Probable" addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world. Our course "The Art of the Probable" addresses the history of scientific ideas, in particular the emergence and development of mathematical probability. But it is neither meant to be a history of the exact sciences per se nor an annex to, say, the Course 6 curriculum in probability and statistics. Rather, our objective is to focus on the formal, thematic, and rhetorical features that imaginative literature shares with texts in the history of probability. These shared issues include (but are not limited to): the attempt to quantify or otherwise explain the presence of chance, risk, and contingency in everyday life; the deduction of causes for phenomena that are knowable only in their effects; and, above all, the question of what it means to think and act rationally in an uncertain world. Our courseSubjects

philosophy | philosophy | scientific thought | scientific thought | scientific method | scientific method | mathematics | mathematics | chance | chance | risk | risk | statistics | statistics | history of science | history of science | quantitative measurement | quantitative measurement | chaos | chaos | uncertainty | uncertainty | induction | induction | deduction | deduction | inference | inference | luck | luck | gambling | gambling | cause and effect | cause and effect | games of chance | games of chance | fate | fate | prediction | prediction | rationality | rationality | decision making | decision making | religion | religion | randomness | randomness | knowledge | knowledge | fact | fact | human nature | human nature | mind | mind | senses | senses | intelligence | intelligence | metaphor | metaphor | Darwinism | DarwinismLicense

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 offers an advanced survey of current debates about the ontology, methodology, and aims of the social sciences. This course offers an advanced survey of current debates about the ontology, methodology, and aims of the social sciences.Subjects

Ontology | Ontology | methodology | methodology | social science | social science | human being | human being | human behavior | human behavior | social structure | social structure | practices | practices | norms | norms | institutions | institutions | individual | individual | society | society | mental state | mental state | values | values | theory | theory | objectivity | objectivity | reductionism | reductionism | individualism | individualism | holism | holism | prediction | prediction | laws | laws | explanation | explanation | rational choice | rational choice | functional | functionalLicense

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 team-taught subject is for doctoral students working on emerging technologies at the interface of technology, policy and societal issues. It integrates concepts of research strategy and design from a variety of disciplines. The class addresses problem identification and formulation of research topics, the role of qualitative and quantitative research methods, and the use of various data collection techniques. Coursework focuses on students' thesis proposals, faculty-student study panels, critical evaluation of research design, and ethical issues in conducting research and gathering data. This team-taught subject is for doctoral students working on emerging technologies at the interface of technology, policy and societal issues. It integrates concepts of research strategy and design from a variety of disciplines. The class addresses problem identification and formulation of research topics, the role of qualitative and quantitative research methods, and the use of various data collection techniques. Coursework focuses on students' thesis proposals, faculty-student study panels, critical evaluation of research design, and ethical issues in conducting research and gathering data.Subjects

ESD.85 | ESD.85 | 17.312 | 17.312 | STS.461 | STS.461 | uncertainty | uncertainty | risk | risk | politics | politics | forecasting | forecasting | future | future | prediction | prediction | disaster | disaster | space shuttle | space shuttle | nuclear power | nuclear power | nuclear energy | nuclear energy | energy policy | energy policy | government | government | technology assessment | technology assessment | OTA | OTA | GPS | GPS | internet | internet | packet switching | packet switching | data network | data network | military | military | DEC | DEC | Digital Equipment | Digital Equipment | drug | drug | pharmaceutical | pharmaceutical | air transport | air transport | aircraft | aircraft | public policy | public policy | industrial policy | industrial policy | UAV | UAV | decision | decisionLicense

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 explores the theory and practice of scientific modeling in the context of auditory and speech biophysics. Based on seminar-style discussions of the research literature, the class draws on examples from hearing and speech, and explores general, meta-theoretical issues that transcend the particular subject matter. Examples include: What is a model? What is the process of model building? What are the different approaches to modeling? What is the relationship between theory and experiment? How are models tested? What constitutes a good model? This course explores the theory and practice of scientific modeling in the context of auditory and speech biophysics. Based on seminar-style discussions of the research literature, the class draws on examples from hearing and speech, and explores general, meta-theoretical issues that transcend the particular subject matter. Examples include: What is a model? What is the process of model building? What are the different approaches to modeling? What is the relationship between theory and experiment? How are models tested? What constitutes a good model?Subjects

hearing | hearing | speech | speech | modeling biology | modeling biology | network model of the ear | network model of the ear | model building | model building | dimensional analysis and scaling | dimensional analysis and scaling | resampling | resampling | monte carlo | monte carlo | forward vs. inverse | forward vs. inverse | chaos | chaos | limits of prediction | limits of prediction | hodgkin | hodgkin | huxley | huxley | molecular mathematic biology | molecular mathematic biology | cochlear input impedance | cochlear input impedance | auditory network | auditory network | auditory morphology | auditory morphology | electric model of neural cell fiber | electric model of neural cell fiber | electric diagrams of neural cells | electric diagrams of neural cells | linear regression | linear regression | sensitivity analysis | sensitivity analysis | cochlea | cochlea | inner ear | inner ear | middle ear | middle ear | auditory cortex | auditory cortex | scientific literature | scientific literature | analysis | analysis | paper analysis | paper analysis | tent maps | tent maps | quadratic maps | quadratic mapsLicense

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 metadataSP.256 The Coming Years (MIT) SP.256 The Coming Years (MIT)

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Explore the future through modeling, reading, and discussion in an open-ended seminar! Our fields of interest will include changes in science and technology, culture and lifestyles, and dominant paradigms and societies. Explore the future through modeling, reading, and discussion in an open-ended seminar! Our fields of interest will include changes in science and technology, culture and lifestyles, and dominant paradigms and societies.Subjects

futurology | futurology | historiography | historiography | change | change | fractals | fractals | nuclear war | nuclear war | global warming | global warming | bioterrorism | bioterrorism | singularity | singularity | politics | politics | memetics | memetics | demographics | demographics | power laws | power laws | recent past | recent past | near future | near future | prediction | prediction | history | history | revolution | revolution | memes | memesLicense

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 metadata7.91J Foundations of Computational and Systems Biology (MIT)

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

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 metadataSocial Media Research for Policy Making (Knowledge Exchange Seminar)

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Carl Miller discusses development of effective social media research for policy making during a seminar on quantitative methods in social media research held at the OII on 26 September 2012. A team of CASM staff and experts used Facebook, Twitter and YouTube to develop (a) a predictive analytic to predict the outcome of each week's vote on X-Factor based on social media users' conversations online, and (b) a real-time visualization of the audience's reaction to each contestant as they sang. The predictive analytic modelled two underlying variables: voter sentiment and voter sediment. This is based on the psephological insight that people can vote either due to the 'sediment' of a longer-term and established loyalty for a contestant, or on the short-term 'appraisal' of their immediat Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/Subjects

social media research | visualisation | big data | social media | twitter | YouTube | prediction | quantitative methods | sentiment | facebook | knowledge exchange | internet | policy | voting | social media research | visualisation | big data | social media | twitter | YouTube | prediction | quantitative methods | sentiment | facebook | knowledge exchange | internet | policy | votingLicense

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Patty Kostkova discusses Twitter-based early warning and risk communication of the 2009 swine flu pandemic during a seminar on quantitative methods in social media research held at the OII on 26 September 2012. The need to improve global population monitoring and enhance surveillance of infectious diseases has never been more pressing. Factors such as air travel act as a catalyst in the spread of new and novel viruses. The unprecedented user-generated activity on social networks and online media over the last few years has created real time streams of personal user data which provides an invaluable tool for monitoring and sampling large populations. Epidemic Intelligence relays on the constant monitoring of online media sources for early warning, detection and rapid response; however, the Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/Subjects

early warning | social media research | swine flu | big data | social media | communication | twitter | disease | pandemic | epidemic | quantitative methods | prediction | Epidemiology | Health | knowledge exchange | internet | outbreak | visualisation | web 2.0 | risk | early warning | social media research | swine flu | big data | social media | communication | twitter | disease | pandemic | epidemic | quantitative methods | prediction | Epidemiology | Health | knowledge exchange | internet | outbreak | visualisation | web 2.0 | riskLicense

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See all metadataSocial Media Research for Policy Making (Knowledge Exchange Seminar)

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Carl Miller discusses development of effective social media research for policy making during a seminar on quantitative methods in social media research held at the OII on 26 September 2012. A team of CASM staff and experts used Facebook, Twitter and YouTube to develop (a) a predictive analytic to predict the outcome of each week's vote on X-Factor based on social media users' conversations online, and (b) a real-time visualization of the audience's reaction to each contestant as they sang. The predictive analytic modelled two underlying variables: voter sentiment and voter sediment. This is based on the psephological insight that people can vote either due to the 'sediment' of a longer-term and established loyalty for a contestant, or on the short-term 'appraisal' of their immediat Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/Subjects

social media research | visualisation | big data | social media | twitter | YouTube | prediction | quantitative methods | sentiment | facebook | knowledge exchange | internet | policy | voting | social media research | visualisation | big data | social media | twitter | YouTube | prediction | quantitative methods | sentiment | facebook | knowledge exchange | internet | policy | votingLicense

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