Description

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports. 9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Subjects

cognitive science | cognitive science | human perception | human perception | cognition | cognition | statistical analysis | statistical analysis | signal detection theory | signal detection theory | single factor design | single factor design | factorial design | factorial design | matlab | matlab | correlational studies | correlational studies | ethics in research | ethics in research | visual cognition | visual cognition | thought | thought | psychology and cognitive science | psychology and cognitive science | information processing | information processing | organization of visual cognitive abilities. | organization of visual cognitive abilities.

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This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives. This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

Subjects

brain | brain | behavioral | behavioral | perception | perception | attention | attention | working memory | working memory | recognition | recognition | recall | recall | language | language | cognitive science | cognitive science | computation | computation | visual perception | visual perception | memory | memory | cognitive architecture | cognitive architecture | learning | learning | reasoning | reasoning | decision-making | decision-making | cognitive development | cognitive development | behavioral perspective | behavioral perspective | computational perspective | computational perspective | neural perspective | neural perspective

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

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What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding. What are the circuits, mechanisms and representations that permit the recognition of a visual scene from just one glance? In this one-day seminar on Scene Understanding, speakers from a variety of disciplines - neurophysiology, cognitive neuroscience, visual cognition, computational neuroscience and computer vision - will address a range of topics related to scene recognition, including natural image categorization, contextual effects on object recognition, and the role of attention in scene understanding and visual art. The goal is to encourage exchanges between researchers of all fields of brain sciences in the burgeoning field of scene understanding.

Subjects

circuits | mechanisms and representation | circuits | mechanisms and representation | recognition of a visual scene | recognition of a visual scene | Scene Understanding | Scene Understanding | neurophysiology | neurophysiology | cognitive neuroscience | cognitive neuroscience | visual cognition | visual cognition | computational neuroscience | computational neuroscience | computer vision | computer vision | natural image categorization | natural image categorization | contextual effects on object recognition | contextual effects on object recognition | role of attention in scene understanding | role of attention in scene understanding

License

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This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving. This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the topics include the nature of mental representation and processing, the architecture of memory, pattern recognition, attention, imagery and mental codes, concepts and prototypes, reasoning and problem solving.

Subjects

cognitive science | cognitive science | cognitive processes | cognitive processes | cognition | cognition | the mind | the mind | object recognition | object recognition | attention | attention | memory | memory | associative memory | associative memory | learning | learning | implicit memory | implicit memory | conceptual short term memory | conceptual short term memory | working memory | working memory | language | language | concepts | concepts | prototypes | prototypes | psycholinguistics | psycholinguistics | visual knowledge | visual knowledge | mental codes | mental codes | judgement | judgement | reasoning | reasoning | problem-solving | problem-solving | conscious thought | conscious thought | unconscious thought | unconscious thought

License

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La Estimulación Cognitiva es una disciplina de intervención psicológica que integra todo un conjunto de técnicas y estrategias sistemáticas y estandarizadas que tienen por objetivo activar y ejercitar las distintas capacidades y funciones cognitivas del individuo con el fin último de mejorar su rendimiento. La Estimulación Cognitiva puede ser aplicada a cualquier individuo, puesto que cualquiera de nosotros podemos mejorar nuestras capacidades para ser más hábiles y diestros; pero su objetivo es también en muchas ocasiones terapéutico, puesto que muchas poblaciones que manifiestan algún tipo de déficit o deterioro cognitivo importante y significativo -discapacidad intelectual, trastornos del desarrollo, personas con daño cerebral, demencias, etc.- pueden, en mayor o menor med La Estimulación Cognitiva es una disciplina de intervención psicológica que integra todo un conjunto de técnicas y estrategias sistemáticas y estandarizadas que tienen por objetivo activar y ejercitar las distintas capacidades y funciones cognitivas del individuo con el fin último de mejorar su rendimiento. La Estimulación Cognitiva puede ser aplicada a cualquier individuo, puesto que cualquiera de nosotros podemos mejorar nuestras capacidades para ser más hábiles y diestros; pero su objetivo es también en muchas ocasiones terapéutico, puesto que muchas poblaciones que manifiestan algún tipo de déficit o deterioro cognitivo importante y significativo -discapacidad intelectual, trastornos del desarrollo, personas con daño cerebral, demencias, etc.- pueden, en mayor o menor med

Subjects

ón cognitiva | ón cognitiva | Entrenamiento cognitivo | Entrenamiento cognitivo | Capacidades cognitivas | Capacidades cognitivas | écnicas de Intervención y Tratamiento Psicológico | écnicas de Intervención y Tratamiento Psicológico | Psicoestimulacion | Psicoestimulacion

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http://creativecommons.org/licenses/by-nc-sa/3.0/

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This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. It develops a critical understanding of experimental design. The course discusses how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds. It provides instruction and practice in written communication as needed for cognitive science research (including critical reviews of journal papers, a literature review and an original research proposal), as well as instruction and practice in oral communication in the form of a poster presentation of a journal paper. This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. It develops a critical understanding of experimental design. The course discusses how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds. It provides instruction and practice in written communication as needed for cognitive science research (including critical reviews of journal papers, a literature review and an original research proposal), as well as instruction and practice in oral communication in the form of a poster presentation of a journal paper.

Subjects

infant cognition | infant cognition | early childhood cognition | early childhood cognition | cognitive development | cognitive development | developmental psychology | developmental psychology | psychology | psychology | developmental research | developmental research | Piaget | Piaget | object knowledge | object knowledge | object individuation | object individuation | object concept | object concept | agents | agents | causal knowledge | causal knowledge | theory of mind | theory of mind | causation | causation | causal transformations | causal transformations

License

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

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

Subjects

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

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The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering. The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

Subjects

comonent analysis | comonent analysis | PCA | PCA | ICA | ICA | fourier analysis | fourier analysis | vision | vision | machine vision | machine vision | pattern matching | pattern matching | pattern analysis | pattern analysis | pattern recognition | pattern recognition | scene analysis | scene analysis | tracking | tracking | feature extraction | feature extraction | color | color | color space | color space | clustering | clustering | bayesian decisions | bayesian decisions | gesture recognition | gesture recognition | action recognition | action recognition | image processing | image processing | image formation | image formation | density estimation | density estimation | classification | classification | morphable models | morphable models | component analysis | component analysis

License

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Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems. Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | object recognition | neural | neural | computation | computation | representation | representation | 3-D objects | 3-D objects | 2-D images | 2-D images | face recognition | face recognition | human face recognition | human face recognition | artificial computational systems | artificial computational systems

License

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

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Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems. Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational, and applied domains. Emphasizes the problem of representation, exploring the issue of how 3-D objects should be encoded so as to efficiently recognize them from 2-D images. Second half focuses on face recognition, an ecologically important instance of the general object recognition problem. Describes experimental studies of human face recognition performance and recent attempts to mimic this ability in artificial computational systems.

Subjects

object recognition | object recognition | neural | neural | computation | computation | representation | representation | 3-D objects | 3-D objects | 2-D images | 2-D images | face recognition | face recognition | human face recognition | human face recognition | artificial computational systems | artificial computational systems

License

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

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This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. Students develop a critical understanding of experimental design and how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds. This course is an introduction to cognitive development focusing on children's understanding of objects, agents, and causality. Students develop a critical understanding of experimental design and how developmental research might address philosophical questions about the origins of knowledge, appearance and reality, and the problem of other minds.

Subjects

infant cognition | infant cognition | early childhood cognition | early childhood cognition | cognitive development | cognitive development | developmental psychology | developmental psychology | psychology | psychology | developmental research | developmental research | Piaget | Piaget | object knowledge | object knowledge | object individuation | object individuation | object concept | object concept | agents | agents | causal knowledge | causal knowledge | theory of mind | theory of mind | causation | causation | causal transformations | causal transformations

License

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

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9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Subjects

cognitive science | human perception | cognition | statistical analysis | signal detection theory | single factor design | factorial design | matlab | correlational studies | ethics in research | visual cognition | thought | psychology and cognitive science | information processing | organization of visual cognitive abilities.

License

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

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This course will lay a foundation in autism theory and autism technology that significantly leverages and expands the Media Lab's ability to pioneer new technology. Students will not only develop new technologies, but also understand, help, and learn from people with autism, a fast-growing group that the CDC identified in the year 2005 as involving an estimated 1 in 150 school-age children ages 6-21. Students will gain an understanding of the basic challenges faced by people diagnosed with autism spectrum disorders, together with their families and caregivers, and an understanding of the fundamental theories that inform therapies and technologies for improving the autistic experience. The course will also explore the converging challenges and goals of autism research and the development o This course will lay a foundation in autism theory and autism technology that significantly leverages and expands the Media Lab's ability to pioneer new technology. Students will not only develop new technologies, but also understand, help, and learn from people with autism, a fast-growing group that the CDC identified in the year 2005 as involving an estimated 1 in 150 school-age children ages 6-21. Students will gain an understanding of the basic challenges faced by people diagnosed with autism spectrum disorders, together with their families and caregivers, and an understanding of the fundamental theories that inform therapies and technologies for improving the autistic experience. The course will also explore the converging challenges and goals of autism research and the development o

Subjects

social interaction | social interaction | communication deficits | communication deficits | people sense | people sense | social cognition | social cognition | embodied cognition | embodied cognition | social skills intervention | social skills intervention | asperger syndrome | asperger syndrome | autism spectrum disorder | autism spectrum disorder | systemizing | systemizing | empathizing | empathizing

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This class explores the social relevance of neuroscience, considering how emerging areas of brain research reflect and reshape social attitudes and agendas. Topics include brain imaging and popular media; neuroscience of empathy, trust, and moral reasoning; new fields of neuroeconomics and neuromarketing; ethical implications of neurotechnologies such as cognitive enhancement pharmaceuticals; neuroscience in the courtroom; and neuroscientific recasting of social problems such as addiction and violence. Guest lectures by neuroscientists, class discussion, and weekly readings in neuroscience, popular media, and science studies. This class explores the social relevance of neuroscience, considering how emerging areas of brain research reflect and reshape social attitudes and agendas. Topics include brain imaging and popular media; neuroscience of empathy, trust, and moral reasoning; new fields of neuroeconomics and neuromarketing; ethical implications of neurotechnologies such as cognitive enhancement pharmaceuticals; neuroscience in the courtroom; and neuroscientific recasting of social problems such as addiction and violence. Guest lectures by neuroscientists, class discussion, and weekly readings in neuroscience, popular media, and science studies.

Subjects

cognitive science | cognitive science | evolutionary psychology | evolutionary psychology | neurobiology | neurobiology | imaging | imaging | MRI | MRI | CT scan | CT scan | fMRI | fMRI | brain | brain | mind | mind | impluse | impluse | brain imaging | brain imaging | morality | morality | moral reasoning | moral reasoning | decision making | decision making | intelligence | intelligence | empathy | empathy | trust | trust | religion | religion | love | love | emotion | emotion | gender differences | gender differences | sexuality | sexuality | stress | stress | prejudice | prejudice | mental focus | mental focus | psychopharmaceuticals | psychopharmaceuticals | antidepressant | antidepressant | neuroeconomics | neuroeconomics | neuromarketing | neuromarketing | neurotheology | neurotheology | cognitive enhancement | cognitive enhancement | witness | witness | courtroom testimony | courtroom testimony | addiction | addiction | violence | violence | learning | learning | behavior | behavior

License

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

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9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports. 9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Subjects

cognitive science | cognitive science | human perception | human perception | cognition | cognition | statistical analysis | statistical analysis | signal detection theory | signal detection theory | single factor design | single factor design | factorial design | factorial design | matlab | matlab | correlational studies | correlational studies | ethics in research | ethics in research

License

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

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Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research. Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research.

Subjects

machine and human learning | machine and human learning | unsupervised learning and clustering | unsupervised learning and clustering | non-parametric methods | non-parametric methods | Bayesian estimation | Bayesian estimation | maximum likelihood | maximum likelihood | statistical classification | statistical classification | decision theory | decision theory | physiological analysis | physiological analysis | computer vision | computer vision | peech recognition and understanding | peech recognition and understanding | recognition | recognition | numerical data | numerical data | 1.126 | 1.126

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The course is directed towards advanced undergraduate and beginning graduate students. It will focus on applications of pattern recognition techniques to problems of machine vision.The topics covered in the course include:Overview of problems of machine vision and pattern classificationImage formation and processingFeature extraction from imagesBiological object recognitionBayesian decision theoryClustering The course is directed towards advanced undergraduate and beginning graduate students. It will focus on applications of pattern recognition techniques to problems of machine vision.The topics covered in the course include:Overview of problems of machine vision and pattern classificationImage formation and processingFeature extraction from imagesBiological object recognitionBayesian decision theoryClustering

Subjects

pattern recognition | pattern recognition | machine vision | machine vision | pattern classification | pattern classification | Image formation | Image formation | processing | processing | feature extraction | feature extraction | Biological object recognition | Biological object recognition | Bayesian Decision Theory | Bayesian Decision Theory | Clustering | Clustering

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

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Includes audio/video content: AV special element audio. 6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts. Part I deals with background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation. Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. Part III compares and contrasts the various approaches to speech recognition, and describes advanced techniques used for acoustic-phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing. Includes audio/video content: AV special element audio. 6.345 introduces students to the rapidly developing field of automatic speech recognition. Its content is divided into three parts. Part I deals with background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation. Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. Part III compares and contrasts the various approaches to speech recognition, and describes advanced techniques used for acoustic-phonetic modelling, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing.

Subjects

speech recognition | speech recognition | automatic speech recognition | automatic speech recognition | acoustic theory | acoustic theory | speech production | speech production | acoustic-phonetics | acoustic-phonetics | signal representation | signal representation | pattern classification | pattern classification | search algorithms | search algorithms | stochastic modelling | stochastic modelling | language modelling | language modelling | speaker adaptation | speaker adaptation | paralinguistic information | paralinguistic information | speech understanding | speech understanding | multimodal processing | multimodal processing

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6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course. 6.803/6.833 is a course in the department's "Artifical Intelligence and Applications" concentration. This course is offered both to undergraduates (6.803) and graduates (6.833). 6.803/6.833 is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | Human Intelligence Enterprise | artificial intelligence | artificial intelligence | computational models | computational models | perception | perception | cognition | cognition | neuroscience | neuroscience | human behavior | human behavior | communication | communication | heuristics | heuristics | object tracking | object tracking | object recognition | object recognition | change representation | change representation | language evolution | language evolution | Turing | Turing | Minsky | Minsky

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

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The course focuses on experimental investigations of speech processes. Topics include: measurement of articulatory movements, measurements of pressures and airflows in speech production, computer-aided waveform analysis and spectral analysis of speech, synthesis of speech, perception and discrimination of speechlike sounds, speech prosody, models for speech recognition, speech disorders, and other topics. Two 1-hour lectures per week Two labs per week Brief lab reports Term project, with short term paper No exams The course focuses on experimental investigations of speech processes. Topics include: measurement of articulatory movements, measurements of pressures and airflows in speech production, computer-aided waveform analysis and spectral analysis of speech, synthesis of speech, perception and discrimination of speechlike sounds, speech prosody, models for speech recognition, speech disorders, and other topics. Two 1-hour lectures per week Two labs per week Brief lab reports Term project, with short term paper No exams

Subjects

Speech | Speech | speech disorders | speech disorders | speech recognition | speech recognition | speech prosody | speech prosody | waveform analysis | waveform analysis | spectral analysis | spectral analysis | 6.542 | 6.542 | 24.966 | 24.966 | HST.712 | HST.712 | Experimental investigations of speech processes | Experimental investigations of speech processes | Topics: measurement of articulatory movements | Topics: measurement of articulatory movements | measurements of pressures and airflows in speech production | measurements of pressures and airflows in speech production | computer-aided waveform analysis and spectral analysis of speech | computer-aided waveform analysis and spectral analysis of speech | synthesis of speech | synthesis of speech | perception and discrimination of speechlike sounds | perception and discrimination of speechlike sounds | models for speech recognition | models for speech recognition | and other topics | and other topics | other topics | other topics

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

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This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course. This course is offered both to undergraduates (6.803) and graduates (6.833) and is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because it focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Subjects

Human Intelligence Enterprise | Human Intelligence Enterprise | artificial intelligence | artificial intelligence | computational models | computational models | perception | perception | cognition | cognition | neuroscience | neuroscience | human behavior | human behavior | communication | communication | heuristics | heuristics | object tracking | object tracking | object recognition | object recognition | change representation | change representation | language evolution | language evolution | Turing | Turing | Minsky | Minsky

License

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

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This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. In 1971, President Nixon declared the "War on Cancer," but after three decades the war is still raging. How much progress have we made toward winning the war and what are we doing to improve the fight? Understanding the molecular and cellular events involved in tumor formation, progression, and metastasis is crucial to the development of innovative therapy for cancer patients. Insights into these processes have been gleaned through basic research using biochemical, molecular, and genetic ana This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. In 1971, President Nixon declared the "War on Cancer," but after three decades the war is still raging. How much progress have we made toward winning the war and what are we doing to improve the fight? Understanding the molecular and cellular events involved in tumor formation, progression, and metastasis is crucial to the development of innovative therapy for cancer patients. Insights into these processes have been gleaned through basic research using biochemical, molecular, and genetic ana

Subjects

cancer | cancer | tumor | tumor | metastasis | metastasis | genetic analysis | genetic analysis | cancer biology | cancer biology | model organisms | model organisms | genetic pathways | genetic pathways | uncontrolled growth | uncontrolled growth | tumor suppressor genes | tumor suppressor genes | oncogenes | oncogenes | tumor initiation | tumor initiation | cell cycle | cell cycle | chromosomal aberration | chromosomal aberration | apoptosis | apoptosis | cell death | cell death | signal transduction pathways | signal transduction pathways | proto-oncogene | proto-oncogene | mutation | mutation | DNA mismatch repair | DNA mismatch repair | telomeres | telomeres | mouse models | mouse models | tissue specificity | tissue specificity | malignancy | malignancy | stem cells | stem cells | therapeutic resistance | therapeutic resistance | differentiation | differentiation | caner research | caner research | cancer therapeutics | cancer therapeutics | chemotherapy | chemotherapy

License

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

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

Subjects

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

License

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

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Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning. Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning.

Subjects

vision | vision | object recognition | object recognition | monkey versus human | monkey versus human | object representations | object representations | fMRI | fMRI | temporal lobe | temporal lobe | visual cortex | visual cortex | neuronal representations | neuronal representations | neurophysiology | neurophysiology | retinal image | retinal image | pattern recognition | pattern recognition | perceptual awareness | perceptual awareness

License

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

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This course is the second half of the intensive survey of brain and behavioral studies for first-year graduate students in the Brain and Cognitive Sciences curriculum. Each module of this core course involves a series of overview lectures by leading researchers in the field. By offering a thorough introduction to the current state of the discipline while emphasizing critical thinking, the course aims to prepare students as cognitive scientists. Topics include: perception, attention, working memory, recognition and recall, language, and other issues in cognitive science. Topics are covered from the neural, behavioral and computational perspectives. This course is the second half of the intensive survey of brain and behavioral studies for first-year graduate students in the Brain and Cognitive Sciences curriculum. Each module of this core course involves a series of overview lectures by leading researchers in the field. By offering a thorough introduction to the current state of the discipline while emphasizing critical thinking, the course aims to prepare students as cognitive scientists. Topics include: perception, attention, working memory, recognition and recall, language, and other issues in cognitive science. Topics are covered from the neural, behavioral and computational perspectives.

Subjects

brain | brain | brain | | brain | | behavioral | | behavioral | | behavioral | behavioral | perception | perception | attention | attention | working memory | working memory | recognition | recognition | recall | recall | language | language | cognitive science | cognitive science | computation | computation

License

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

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