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6.830 Database Systems (MIT) 6.830 Database Systems (MIT)

Description

This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken MIT course 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend. Topics related to the engineering and design of database systems, including: data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel, and he This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken MIT course 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend. Topics related to the engineering and design of database systems, including: data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel, and he

Subjects

engineering and design of database systems | data models | engineering and design of database systems | data models | database and schema design | database and schema design | schema normalization and integrity constraints | schema normalization and integrity constraints | query processing | query processing | query optimization and cost estimation | query optimization and cost estimation | transactions | transactions | recovery | recovery | concurrency control | concurrency control | isolation and consistency | isolation and consistency | distributed | distributed | parallel | parallel | heterogeneous databases | heterogeneous databases | adaptive databases | adaptive databases | trigger systems | trigger systems | pub-sub systems | pub-sub systems | semi structured data and XML querying | semi structured data and XML querying

License

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6.893 Database Systems (MIT) 6.893 Database Systems (MIT)

Description

This course is designed to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, query optimization, query processing, and transactions. This is not a course on database design or SQL programming (though we will discuss these issues briefly). It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend. This course is designed to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, query optimization, query processing, and transactions. This is not a course on database design or SQL programming (though we will discuss these issues briefly). It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend.

Subjects

database systems | database systems | data models | data models | database design | database design | schema design | schema design | schema normalization | schema normalization | integrity constraints | integrity constraints | query processing | query processing | query optimization | query optimization | cost estimation | cost estimation | transactions | transactions | recovery | recovery | concurrency control | concurrency control | isolation | isolation | consistency | consistency | distributed | parallel | and heterogeneous databases | distributed | parallel | and heterogeneous databases | adaptive databases | adaptive databases | trigger systems | trigger systems | pub-sub systems | pub-sub systems | semi-structured data | semi-structured data | XML querying | XML querying

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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20.453J Biomedical Information Technology (BE.453J) (MIT) 20.453J Biomedical Information Technology (BE.453J) (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | imaging | medical imaging | medical imaging | metadata | metadata | medical record | medical record | DICOM | DICOM | computer architecture | computer architecture | client-server architecture | client-server architecture | SEM | SEM | TEM | TEM | OME | OME | RDF | RDF | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | clinical decision support | clinical decision support | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | 20.453 | 20.453 | 2.771 | 2.771 | HST.958 | HST.958

License

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BE.453J Biomedical Information Technology (MIT) BE.453J Biomedical Information Technology (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | imaging | medical imaging | medical imaging | metadata | metadata | medical record | medical record | DICOM | DICOM | computer architecture | computer architecture | client-server architecture | client-server architecture | SEM | SEM | TEM | TEM | OME | OME | RDF | RDF | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | clinical decision support | clinical decision support | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | 2.771J | 2.771J | 2.771 | 2.771 | HST.958J | HST.958J | HST.958 | HST.958

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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7.346 Synaptic Plasticity and Memory, from Molecules to Behavior (MIT) 7.346 Synaptic Plasticity and Memory, from Molecules to Behavior (MIT)

Description

In this course we will discover how innovative technologies combined with profound hypotheses have given rise to our current understanding of neuroscience. We will study both new and classical primary research papers with a focus on the plasticity between synapses in a brain structure called the hippocampus, which is believed to underlie the ability to create and retrieve certain classes of memories. We will discuss the basic electrical properties of neurons and how they fire. We will see how firing properties can change with experience, and we will study the biochemical basis of these changes. We will learn how molecular biology can be used to specifically change the biochemical properties of brain circuits, and we will see how these circuits form a representation of space giving rise to In this course we will discover how innovative technologies combined with profound hypotheses have given rise to our current understanding of neuroscience. We will study both new and classical primary research papers with a focus on the plasticity between synapses in a brain structure called the hippocampus, which is believed to underlie the ability to create and retrieve certain classes of memories. We will discuss the basic electrical properties of neurons and how they fire. We will see how firing properties can change with experience, and we will study the biochemical basis of these changes. We will learn how molecular biology can be used to specifically change the biochemical properties of brain circuits, and we will see how these circuits form a representation of space giving rise to

Subjects

synapse | synapse | memory | memory | neuroscience | neuroscience | plasticity | plasticity | hippocampus | hippocampus | LTP | LTP | molecular mechanism | molecular mechanism | Morris water maze | Morris water maze | place cells | place cells | NMDA | NMDA | synaptic tagging | synaptic tagging | long term depression | long term depression | cortex | cortex | synaptic plasticity | synaptic plasticity | neuronal circuits | neuronal circuits | specificity | specificity | CA1 | CA1 | grid cells | grid cells | schema | schema | fear memory | fear memory | biochemistry | biochemistry

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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9.675 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

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18.S996 Category Theory for Scientists (MIT) 18.S996 Category Theory for Scientists (MIT)

Description

The goal of this class is to prove that category theory is a powerful language for understanding and formalizing common scientific models. The power of the language will be tested by its ability to penetrate into taken-for-granted ideas, either by exposing existing weaknesses or flaws in our understanding, or by highlighting hidden commonalities across scientific fields. The goal of this class is to prove that category theory is a powerful language for understanding and formalizing common scientific models. The power of the language will be tested by its ability to penetrate into taken-for-granted ideas, either by exposing existing weaknesses or flaws in our understanding, or by highlighting hidden commonalities across scientific fields.

Subjects

Sets | Sets | functions | functions | commutative diagrams | commutative diagrams | products | products | coproducts | coproducts | finite limits | finite limits | monoids | monoids | groups | groups | graphs | graphs | orders | orders | schemas | schemas | instances | instances | databases | databases | categories | categories | functors | functors | mathematics | mathematics | natural transformations | natural transformations | limits | limits | colimits | colimits | adjoint functors | adjoint functors | monads | monads | operads | operads | isomorphism | isomorphism | molecular dynamics | molecular dynamics | olog | olog

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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20.453J Biomedical Information Technology (MIT) 20.453J Biomedical Information Technology (MIT)

Description

This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig

Subjects

20.453 | 20.453 | 2.771 | 2.771 | HST.958 | HST.958 | imaging | imaging | medical imaging | medical imaging | metadata | metadata | molecular biology | molecular biology | medical records | medical records | DICOM | DICOM | RDF | RDF | OWL | OWL | SPARQL | SPARQL | SBML | SBML | CellML | CellML | semantic web | semantic web | BioHaystack | BioHaystack | database | database | schema | schema | ExperiBase | ExperiBase | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics | computational biology | computational biology | clinical decision support | clinical decision support | clinical trial | clinical trial | microarray | microarray | gel electrophoresis | gel electrophoresis | diagnosis | diagnosis | pathway modeling | pathway modeling | XML | XML | SQL | SQL | relational database | relational database | biological data | biological data | ontologies | ontologies | drug development | drug development | drug discovery | drug discovery | drug target | drug target | pharmaceutical | pharmaceutical | gene sequencing | gene sequencing

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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6.830 Database Systems (MIT)

Description

This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. It is designed for students who have taken MIT course 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend. Topics related to the engineering and design of database systems, including: data models; database and schema design; schema normalization and integrity constraints; query processing; query optimization and cost estimation; transactions; recovery; concurrency control; isolation and consistency; distributed, parallel, and he

Subjects

engineering and design of database systems | data models | database and schema design | schema normalization and integrity constraints | query processing | query optimization and cost estimation | transactions | recovery | concurrency control | isolation and consistency | distributed | parallel | heterogeneous databases | adaptive databases | trigger systems | pub-sub systems | semi structured data and XML querying

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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6.893 Database Systems (MIT)

Description

This course is designed to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, query optimization, query processing, and transactions. This is not a course on database design or SQL programming (though we will discuss these issues briefly). It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed though students who have taken an undergraduate course in databases are encouraged to attend.

Subjects

database systems | data models | database design | schema design | schema normalization | integrity constraints | query processing | query optimization | cost estimation | transactions | recovery | concurrency control | isolation | consistency | distributed | parallel | and heterogeneous databases | adaptive databases | trigger systems | pub-sub systems | semi-structured data | XML querying

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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Mind maps - Alcohols Schema

Description

Using Inspiration mind mapping software with students - Alcohols Schema - Please note: if you do not have Inspiration software installed on your computer, view the image of the map (gif), but it will not be interactive.

Subjects

alcohols schema | chemistry | mind maps | .isf | gif | SCIENCES and MATHEMATICS | R

License

Attribution-Noncommercial 2.0 UK: England & Wales Attribution-Noncommercial 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc/2.0/uk/ http://creativecommons.org/licenses/by-nc/2.0/uk/

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Scripts and schemas

Description

Part of eMerge: a programme of E-learning staff development for Scotland's colleges.

Subjects

nursery research | health | social care | scripts and schemas | SCQF Level 1 | SCQF Level 2 | SCQF Level 3 | SCQF Level 4 | SCQF Level 5 | SCQF Level 6 | SCQF Level 7 | SCQF Level 8

License

Attribution-NonCommercial-ShareAlike 4.0 International Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ http://creativecommons.org/licenses/by-nc-sa/4.0/

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20.453J Biomedical Information Technology (BE.453J) (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | medical imaging | metadata | medical record | DICOM | computer architecture | client-server architecture | SEM | TEM | OME | RDF | semantic web | BioHaystack | database | schema | ExperiBase | genomics | proteomics | bioinformatics | clinical decision support | microarray | gel electrophoresis | diagnosis | 20.453 | 2.771 | HST.958

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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BE.453J Biomedical Information Technology (MIT)

Description

The objective of this subject is to teach the design of contemporary information systems for biological and medical data. These data are growing at a prodigious rate, and new information systems are required. This subject will cover examples from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures will be covered. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (C, C++, Java®, Lisp, Perl, Python, etc.). A major term project is

Subjects

imaging | medical imaging | metadata | medical record | DICOM | computer architecture | client-server architecture | SEM | TEM | OME | RDF | semantic web | BioHaystack | database | schema | ExperiBase | genomics | proteomics | bioinformatics | clinical decision support | microarray | gel electrophoresis | diagnosis | 2.771J | 2.771 | HST.958J | HST.958

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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7.346 Synaptic Plasticity and Memory, from Molecules to Behavior (MIT)

Description

In this course we will discover how innovative technologies combined with profound hypotheses have given rise to our current understanding of neuroscience. We will study both new and classical primary research papers with a focus on the plasticity between synapses in a brain structure called the hippocampus, which is believed to underlie the ability to create and retrieve certain classes of memories. We will discuss the basic electrical properties of neurons and how they fire. We will see how firing properties can change with experience, and we will study the biochemical basis of these changes. We will learn how molecular biology can be used to specifically change the biochemical properties of brain circuits, and we will see how these circuits form a representation of space giving rise to

Subjects

synapse | memory | neuroscience | plasticity | hippocampus | LTP | molecular mechanism | Morris water maze | place cells | NMDA | synaptic tagging | long term depression | cortex | synaptic plasticity | neuronal circuits | specificity | CA1 | grid cells | schema | fear memory | biochemistry

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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18.S996 Category Theory for Scientists (MIT)

Description

The goal of this class is to prove that category theory is a powerful language for understanding and formalizing common scientific models. The power of the language will be tested by its ability to penetrate into taken-for-granted ideas, either by exposing existing weaknesses or flaws in our understanding, or by highlighting hidden commonalities across scientific fields.

Subjects

Sets | functions | commutative diagrams | products | coproducts | finite limits | monoids | groups | graphs | orders | schemas | instances | databases | categories | functors | mathematics | natural transformations | limits | colimits | adjoint functors | monads | operads | isomorphism | molecular dynamics | olog

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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20.453J Biomedical Information Technology (MIT)

Description

This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system desig

Subjects

20.453 | 2.771 | HST.958 | imaging | medical imaging | metadata | molecular biology | medical records | DICOM | RDF | OWL | SPARQL | SBML | CellML | semantic web | BioHaystack | database | schema | ExperiBase | genomics | proteomics | bioinformatics | computational biology | clinical decision support | clinical trial | microarray | gel electrophoresis | diagnosis | pathway modeling | XML | SQL | relational database | biological data | ontologies | drug development | drug discovery | drug target | pharmaceutical | gene sequencing

License

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

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9.675 The Development of Object and Face Recognition (MIT)

Description

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

Subjects

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

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