Searching for cognition : 219 results found | RSS Feed for this search

1 2 3 4 5 6 7 8 9

9.459 Scene Understanding Symposium (MIT) 9.459 Scene Understanding Symposium (MIT)

Description

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

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.913 Pattern Recognition for Machine Vision (MIT) 9.913 Pattern Recognition for Machine Vision (MIT)

Description

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

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.67 Object and Face Recognition (MIT) 9.67 Object and Face Recognition (MIT)

Description

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.67 Object and Face Recognition (MIT) 9.67 Object and Face Recognition (MIT)

Description

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.85 Infant and Early Childhood Cognition (MIT) 9.85 Infant and Early Childhood Cognition (MIT)

Description

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

Analysis (MIT) Analysis (MIT)

Description

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

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.85 Infant and Early Childhood Cognition (MIT) 9.85 Infant and Early Childhood Cognition (MIT)

Description

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.63 Laboratory in Visual Cognition (MIT) 9.63 Laboratory in Visual Cognition (MIT)

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.

License

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

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

Description

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

Subjects

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

License

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans (MIT) 9.916 The Neural Basis of Visual Object Recognition in Monkeys and Humans (MIT)

Description

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

6.345 Automatic Speech Recognition (MIT) 6.345 Automatic Speech Recognition (MIT)

Description

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

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.65 Cognitive Processes (MIT) 9.65 Cognitive Processes (MIT)

Description

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

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-9.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

6.803 The Human Intelligence Enterprise (MIT) 6.803 The Human Intelligence Enterprise (MIT)

Description

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

License

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-6.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

6.542J Laboratory on the Physiology, Acoustics, and Perception of Speech (MIT) 6.542J Laboratory on the Physiology, Acoustics, and Perception of Speech (MIT)

Description

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

License

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-6.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

MAS.622J Pattern Recognition and Analysis (MIT) MAS.622J Pattern Recognition and Analysis (MIT)

Description

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the 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. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class. This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the 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. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

Subjects

MAS.622 | MAS.622 | 1.126 | 1.126 | pattern recognition | pattern recognition | feature detection | feature detection | classification | classification | probability theory | probability theory | pattern analysis | pattern analysis | conditional probability | conditional probability | bayes rule | bayes rule | random vectors | decision theory | random vectors | decision theory | ROC curves | ROC curves | likelihood ratio test | likelihood ratio test | fisher discriminant | fisher discriminant | template-based recognition | template-based recognition | feature extraction | feature extraction | eigenvector and multilinear analysis | eigenvector and multilinear analysis | linear discriminant | linear discriminant | perceptron learning | perceptron learning | optimization by gradient descent | optimization by gradient descent | support vecotr machines | support vecotr machines | K-nearest-neighbor classification | K-nearest-neighbor classification | parzen estimation | parzen estimation | unsupervised learning | unsupervised learning | clustering | clustering | vector quantization | vector quantization | K-means | K-means | Expectation-Maximization | Expectation-Maximization | Hidden markov models | Hidden markov models | viterbi algorithm | viterbi algorithm | Baum-Welch algorithm | Baum-Welch algorithm | linear dynamical systems | linear dynamical systems | Kalman filtering | Kalman filtering | Bayesian networks | Bayesian networks | decision trees | decision trees | reinforcement learning | reinforcement learning | genetic algorithms | genetic algorithms

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

MAS.965 Social Visualization (MIT) MAS.965 Social Visualization (MIT)

Description

Millions of people are on-line today and the number is rapidly growing - yet this virtual crowd is often invisible. In this course we will examine ways of visualizing people, their activities and their interactions. Students will study the cognitive and cultural basis for social visualization through readings drawn from sociology, psychology and interface design and they will explore new ways of depicting virtual crowds and mapping electronic spaces through a series of design exercises. Millions of people are on-line today and the number is rapidly growing - yet this virtual crowd is often invisible. In this course we will examine ways of visualizing people, their activities and their interactions. Students will study the cognitive and cultural basis for social visualization through readings drawn from sociology, psychology and interface design and they will explore new ways of depicting virtual crowds and mapping electronic spaces through a series of design exercises.

Subjects

social visualization | social visualization | internet | internet | chat | chat | mediation | mediation | faces | faces | emotion | emotion | emoticons | emoticons | cognition | cognition | recognition | recognition | personality | personality | perception | perception | depiction | depiction | virtual presence | virtual presence | conversation | conversation | rhythym | rhythym | psychology | psychology | representation | representation | design | design | privacy | privacy

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

6.803 The Human Intelligence Enterprise (MIT) 6.803 The Human Intelligence Enterprise (MIT)

Description

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

Site sourced from

http://ocw.mit.edu/rss/all/mit-allcourses-6.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.913-C Pattern Recognition for Machine Vision (MIT) 9.913-C Pattern Recognition for Machine Vision (MIT)

Description

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

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

MAS.962 Autism Theory and Technology (MIT) MAS.962 Autism Theory and Technology (MIT)

Description

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

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.459 Scene Understanding Symposium (MIT)

Description

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

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.67 Object and Face Recognition (MIT)

Description

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 | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | 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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.67 Object and Face Recognition (MIT)

Description

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 | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | 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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.913 Pattern Recognition for Machine Vision (MIT)

Description

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

License

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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.67 Object and Face Recognition (MIT)

Description

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 | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | 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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata

9.67 Object and Face Recognition (MIT)

Description

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 | neural | computation | representation | 3-D objects | 2-D images | face recognition | human face recognition | 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

Site sourced from

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

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata