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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 inferenceLicense

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 metadata6.438 Algorithms for Inference (MIT) 6.438 Algorithms for Inference (MIT)

Description

This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference. This is a graduate-level introduction to the principles of statistical inference with probabilistic models defined using graphical representations. The material in this course constitutes a common foundation for work in machine learning, signal processing, artificial intelligence, computer vision, control, and communication. Ultimately, the subject is about teaching you contemporary approaches to, and perspectives on, problems of statistical inference.Subjects

inference | inference | algorithm | algorithm | graphical model | graphical model | factor graph | factor graph | markov chain | markov chain | Gaussian model | Gaussian model | loopy belief propagation | loopy belief propagation | EM algorithm | EM algorithm | statistical inference | statistical inference | probabilistic graphical model | probabilistic graphical model | Hidden Markov model | Hidden Markov model | linear dynamical systems | linear dynamical systems | Sum-product algorithm | Sum-product algorithm | junction tree algorithm | junction tree algorithm | Forward-backward algorithm | Forward-backward algorithm | Kalman filtering | Kalman filtering | smoothing | smoothing | Variational method | Variational method | mean-field theory | mean-field theory | Min-sum algorithm | Min-sum algorithm | Viterbi algorithm | Viterbi algorithm | parameter estimation | parameter estimation | learning structure | learning structureLicense

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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14.11 is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them. 14.11 is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them.Subjects

racial discrimination | racial discrimination | public health and persuasion | public health and persuasion | incentives | incentives | gender differences in economic environments | gender differences in economic environments | intrinsic motivation and fairness | intrinsic motivation and fairness | educational quality | educational quality | corruption | corruption | learning and social effects | learning and social effects | housing experiments | housing experiments | voting behavior and political economy | voting behavior and political economy | jury advocacy | jury advocacy | causal inference | causal inference | internal and external threats | internal and external threats | clustering | clustering | standard errors | standard errors | randomization | randomization | statistical inference with multiple outcomes | statistical inference with multiple outcomesLicense

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

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6.825 is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence). 6.825 is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).Subjects

artificial intelligence | artificial intelligence | representation | representation | inference | inference | first order logic | first order logic | deterministic planning | deterministic planning | decision-theoretic planning | decision-theoretic planning | supervised learning methods | supervised learning methods | Bayesian network inference | Bayesian network inference | learning | learning | Java | JavaLicense

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

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See all metadata18.441 Statistical Inference (MIT) 18.441 Statistical Inference (MIT)

Description

Reviews probability and introduces statistical inference. Point and interval estimation. The maximum likelihood method. Hypothesis testing. Likelihood-ratio tests and Bayesian methods. Nonparametric methods. Analysis of variance, regression analysis and correlation. Chi-square goodness of fit tests. More theoretical than 18.443 (Statistics for Applications) and more detailed in its treatment of statistics than 18.05 (Introduction to Probability and Statistics). Reviews probability and introduces statistical inference. Point and interval estimation. The maximum likelihood method. Hypothesis testing. Likelihood-ratio tests and Bayesian methods. Nonparametric methods. Analysis of variance, regression analysis and correlation. Chi-square goodness of fit tests. More theoretical than 18.443 (Statistics for Applications) and more detailed in its treatment of statistics than 18.05 (Introduction to Probability and Statistics).Subjects

probability | probability | statistical inference | statistical inference | Point and interval estimation | Point and interval estimation | The maximum likelihood method | The maximum likelihood method | Hypothesis testing | Hypothesis testing | Likelihood-ratio tests | Likelihood-ratio tests | Bayesian methods | Bayesian methods | Nonparametric methods | Nonparametric methods | Analysis of variance | Analysis of variance | regression analysis | regression analysis | correlation | correlation | Chi-square goodness of fit tests | Chi-square goodness of fit tests | Likelihood-ratio tests and Bayesian methods | Likelihood-ratio tests and Bayesian methods | regression analysis and correlation | regression analysis and correlation | probability | statistical inference | probability | statistical inference | Analysis of variance | regression analysis and correlation | Analysis of variance | regression analysis and correlationLicense

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 metadata9.675 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.Subjects

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

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.htmSite sourced from

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See all metadata14.11 Putting Social Sciences to the Test: Field Experiments in Economics (MIT)

Description

14.11 is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them.Subjects

racial discrimination | public health and persuasion | incentives | gender differences in economic environments | intrinsic motivation and fairness | educational quality | corruption | learning and social effects | housing experiments | voting behavior and political economy | jury advocacy | causal inference | internal and external threats | clustering | standard errors | randomization | statistical inference with multiple outcomesLicense

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.htmSite sourced from

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This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference. This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference.Subjects

probabilistic systems | probabilistic systems | probabilistic systems analysis | probabilistic systems analysis | applied probability | applied probability | uncertainty | uncertainty | uncertainty modeling | uncertainty modeling | uncertainty quantification | uncertainty quantification | analysis of uncertainty | analysis of uncertainty | uncertainty analysis | uncertainty analysis | sample space | sample space | random variables | random variables | transform techniques | transform techniques | simple random processes | simple random processes | probability distribution | probability distribution | Markov process | Markov process | limit theorem | limit theorem | statistical inference | statistical inferenceLicense

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 metadata17.869 Political Science Scope and Methods (MIT) 17.869 Political Science Scope and Methods (MIT)

Description

This course is designed to provide an introduction to a variety of empirical research methods used by political scientists. The primary aims of the course are to make you a more sophisticated consumer of diverse empirical research and to allow you to conduct sophisticated independent work in your junior and senior years. This is not a course in data analysis. Rather, it is a course on how to approach political science research. This course is designed to provide an introduction to a variety of empirical research methods used by political scientists. The primary aims of the course are to make you a more sophisticated consumer of diverse empirical research and to allow you to conduct sophisticated independent work in your junior and senior years. This is not a course in data analysis. Rather, it is a course on how to approach political science research.Subjects

political science | political science | empirical research | empirical research | scientific method | scientific method | research design | research design | models | models | samping | samping | statistical analysis | statistical analysis | measurement | measurement | ethics | ethics | empirical | empirical | research | research | scientific | scientific | methods | methods | statistics | statistics | statistical | statistical | analysis | analysis | political | political | politics | politics | science | science | design | design | sampling | sampling | theoretical | theoretical | observation | observation | data | data | case studies | case studies | cases | cases | empirical research methods | empirical research methods | political scientists | political scientists | empirical analysis | empirical analysis | theoretical analysis | theoretical analysis | research projects | research projects | department faculty | department faculty | inference | inference | writing | writing | revision | revision | oral presentations | oral presentations | experimental method | experimental method | theories | theories | political implications | political implicationsLicense

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 metadata14.381 Statistical Method in Economics (MIT) 14.381 Statistical Method in Economics (MIT)

Description

This course is divided into two sections, Part I and Part II. Part I provides an introduction to statistical theory and can be found by visiting 14.381 Fall 2013. Part II, found here, prepares students for the remainder of the econometrics sequence. The emphasis of the course is to understand the basic principles of statistical theory. A brief review of probability will be given; however, this material is assumed knowledge. The course also covers basic regression analysis. Topics covered include probability, random samples, asymptotic methods, point estimation, evaluation of estimators, Cramer-Rao theorem, hypothesis tests, Neyman Pearson lemma, Likelihood Ratio test, interval estimation, best linear predictor, best linear approximation, conditional expectation function, buil This course is divided into two sections, Part I and Part II. Part I provides an introduction to statistical theory and can be found by visiting 14.381 Fall 2013. Part II, found here, prepares students for the remainder of the econometrics sequence. The emphasis of the course is to understand the basic principles of statistical theory. A brief review of probability will be given; however, this material is assumed knowledge. The course also covers basic regression analysis. Topics covered include probability, random samples, asymptotic methods, point estimation, evaluation of estimators, Cramer-Rao theorem, hypothesis tests, Neyman Pearson lemma, Likelihood Ratio test, interval estimation, best linear predictor, best linear approximation, conditional expectation function, builSubjects

statistical theory | statistical theory | econometrics | econometrics | regression analysis | regression analysis | probability | probability | random samples | random samples | asymptotic methods | asymptotic methods | point estimation | point estimation | evaluation of estimators | evaluation of estimators | Cramer-Rao theorem | Cramer-Rao theorem | hypothesis tests | hypothesis tests | Neyman Pearson lemma | Neyman Pearson lemma | Likelihood Ratio test | Likelihood Ratio test | interval estimation | interval estimation | best linear predictor | best linear predictor | best linear approximation | best linear approximation | conditional expectation function | conditional expectation function | building functional forms | building functional forms | regression algebra | regression algebra | Gauss-Markov optimality | Gauss-Markov optimality | finite-sample inference | finite-sample inference | consistency | consistency | asymptotic normality | asymptotic normality | heteroscedasticity | heteroscedasticity | autocorrelation | autocorrelationLicense

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 metadata14.33 Economics Research and Communication (MIT) 14.33 Economics Research and Communication (MIT)

Description

This course is for students interested in conducting original research on economics questions. There will be an emphasis on choice of research topics, primary sources, data sources, and research methods. The primary activities are oral presentations, the preparation of a paper, and providing constructive feedback on classmates' research projects. This course is for students interested in conducting original research on economics questions. There will be an emphasis on choice of research topics, primary sources, data sources, and research methods. The primary activities are oral presentations, the preparation of a paper, and providing constructive feedback on classmates' research projects.Subjects

research | research | communication | communication | economics | economics | causation | causation | randomized experiments | randomized experiments | inference | inference | STATA | STATA | empirical | empirical | writing | writingLicense

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See all metadata6.820 Fundamentals of Program Analysis (MIT) 6.820 Fundamentals of Program Analysis (MIT)

Description

This course offers a comprehensive introduction to the field of program analysis. It covers some of the major forms of program analysis including Type Checking, Abstract Interpretation and Model Checking. For each of these, the course covers the underlying theories as well as modern techniques and applications. This course offers a comprehensive introduction to the field of program analysis. It covers some of the major forms of program analysis including Type Checking, Abstract Interpretation and Model Checking. For each of these, the course covers the underlying theories as well as modern techniques and applications.Subjects

program analysis | program analysis | Lambda Calculus | Lambda Calculus | Semantics | Semantics | λlet calculus | λlet calculus | Hindley-Milner type inference | Hindley-Milner type inference | Monads | Monads | Axiomatic Semantics | Axiomatic Semantics | Dataflow Analysis | Dataflow Analysis | Type Checking | Type Checking | Abstract Interpretation | Abstract Interpretation | Model Checking | Model CheckingLicense

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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Includes audio/video content: AV lectures. This course introduces students to the modeling, quantification, and analysis of uncertainty. The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management. Includes audio/video content: AV lectures. This course introduces students to the modeling, quantification, and analysis of uncertainty. The tools of probability theory, and of the related field of statistical inference, are the keys for being able to analyze and make sense of data. These tools underlie important advances in many fields, from the basic sciences to engineering and management.Subjects

probability | probability | probability models | probability models | bayes rule | bayes rule | discrete random variables | discrete random variables | continuous random variables | continuous random variables | bernoulli process | bernoulli process | poisson process | poisson process | markov chains | markov chains | central limit theorem | central limit theorem | statistical inference | statistical inferenceLicense

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 metadata6.825 Techniques in Artificial Intelligence (SMA 5504) (MIT)

Description

6.825 is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5504 (Techniques in Artificial Intelligence).Subjects

artificial intelligence | representation | inference | first order logic | deterministic planning | decision-theoretic planning | supervised learning methods | Bayesian network inference | learning | JavaLicense

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.htmSite sourced from

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

Description

Includes audio/video content: AV lectures. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective. Includes audio/video content: AV lectures. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Subjects

artificial intelligence | artificial intelligence | knowledge representation | knowledge representation | problem solving | problem solving | learning methods | learning methods | intelligent systems | intelligent systems | basic search | basic search | optimal search | optimal search | neural nets | neural nets | genetic algorithms | genetic algorithms | support vector machines | support vector machines | boosting | boosting | probabilistic inference | probabilistic inferenceLicense

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 statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics. This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.Subjects

15.075 | 15.075 | ESD.07 | ESD.07 | statistics | statistics | data analysis | data analysis | multiple regression | multiple regression | analysis of variance | analysis of variance | multivariate analysis | multivariate analysis | data mining | data mining | probability | probability | collecting data | collecting data | sampling distributions | sampling distributions | inference | inference | linear regression | linear regression | ANOVA | ANOVA | chi-square test | chi-square testLicense

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

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See all metadata18.441 Statistical Inference (MIT)

Description

Reviews probability and introduces statistical inference. Point and interval estimation. The maximum likelihood method. Hypothesis testing. Likelihood-ratio tests and Bayesian methods. Nonparametric methods. Analysis of variance, regression analysis and correlation. Chi-square goodness of fit tests. More theoretical than 18.443 (Statistics for Applications) and more detailed in its treatment of statistics than 18.05 (Introduction to Probability and Statistics).Subjects

probability | statistical inference | Point and interval estimation | The maximum likelihood method | Hypothesis testing | Likelihood-ratio tests | Bayesian methods | Nonparametric methods | Analysis of variance | regression analysis | correlation | Chi-square goodness of fit tests | Likelihood-ratio tests and Bayesian methods | regression analysis and correlation | probability | statistical inference | Analysis of variance | regression analysis and correlationLicense

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.htmSite sourced from

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See all metadata9.69 Foundations of Cognition (MIT) 9.69 Foundations of Cognition (MIT)

Description

Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project i Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project iSubjects

cognitive science | cognitive science | Western philosophy | Western philosophy | structure | structure | color | color | objects | objects | number | number | similarity | similarity | inductive inference | inductive inference | space | space | time | time | reasoning | reasoning | decision-making | decision-making | morality | morality | consciousness | consciousness | development | development | psychology | psychology | computation | computation | neuroscience | neuroscience | philosophy | philosophy | linguistics | linguistics | artificial intelligence | artificial intelligenceLicense

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.htmSite sourced from

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See all metadata15.450 Analytics of Finance (MIT) 15.450 Analytics of Finance (MIT)

Description

This course covers the key quantitative methods of finance: financial econometrics and statistical inference for financial applications; dynamic optimization; Monte Carlo simulation; stochastic (Itô) calculus. These techniques, along with their computer implementation, are covered in depth. Application areas include portfolio management, risk management, derivatives, and proprietary trading. This course covers the key quantitative methods of finance: financial econometrics and statistical inference for financial applications; dynamic optimization; Monte Carlo simulation; stochastic (Itô) calculus. These techniques, along with their computer implementation, are covered in depth. Application areas include portfolio management, risk management, derivatives, and proprietary trading.Subjects

financial econometrics | financial econometrics | statistical inference | statistical inference | dynamic optimization | dynamic optimization | Monte Carlo simulation | Monte Carlo simulation | stochastic (Itô) calculus | stochastic (Itô) calculus | portfolio management | portfolio management | risk management | risk management | proprietary trading | proprietary trading | derivative pricing | derivative pricing | generalized method of moments | generalized method of moments | Black-Scholes model | Black-Scholes modelLicense

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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See all metadata9.69 Foundations of Cognition (MIT) 9.69 Foundations of Cognition (MIT)

Description

Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project i Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project iSubjects

cognitive science | cognitive science | Western philosophy | Western philosophy | structure | structure | color | color | objects | objects | number | number | similarity | similarity | inductive inference | inductive inference | space | space | time | time | reasoning | reasoning | decision-making | decision-making | morality | morality | consciousness | consciousness | development | development | psychology | psychology | computation | computation | neuroscience | neuroscience | philosophy | philosophy | linguistics | linguistics | artificial intelligence | artificial intelligenceLicense

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See all metadata6.867 Machine Learning (MIT) 6.867 Machine Learning (MIT)

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered. 6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.Subjects

machine learning algorithms | machine learning algorithms | statistical inference | statistical inference | representation | representation | generalization | generalization | model selection | model selection | linear/additive models | linear/additive models | active learning | active learning | boosting | boosting | support vector machines | support vector machines | hidden Markov models | hidden Markov models | Bayesian networks | Bayesian networks | classification | classification | linear regression | linear regression | modern machine learning methods | modern machine learning methodsLicense

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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In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required. In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.Subjects

statistics | statistics | statistical model | statistical model | modelling | modelling | probability | probability | probabilistic model | probabilistic model | risk assessment | risk assessment | system analysis | system analysis | system design | system design | systems engineering | systems engineering | distributions | distributions | poisson | poisson | markov | markov | queuing theory | queuing theory | congestion | congestion | traffic | traffic | regression | regression | hypothesis testing | hypothesis testing | inference | inference | operations research | operations research | Weibull analysis | Weibull 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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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 informationSubjects

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 treesLicense

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

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This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference. This course is offered both to undergraduates (6.041) and graduates (6.431), but the assignments differ. 6.041/6.431 introduces students to the modeling, quantification, and analysis of uncertainty. Topics covered include: formulation and solution in sample space, random variables, transform techniques, simple random processes and their probability distributions, Markov processes, limit theorems, and elements of statistical inference.Subjects

probabilistic systems | probabilistic systems | probabilistic systems analysis | probabilistic systems analysis | applied probability | applied probability | uncertainty | uncertainty | uncertainty modeling | uncertainty modeling | uncertainty quantification | uncertainty quantification | analysis of uncertainty | analysis of uncertainty | uncertainty analysis | uncertainty analysis | sample space | sample space | random variables | random variables | transform techniques | transform techniques | simple random processes | simple random processes | probability distribution | probability distribution | Markov process | Markov process | limit theorem | limit theorem | statistical inference | statistical inferenceLicense

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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