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6.263J Data Communication Networks (MIT) 6.263J Data Communication Networks (MIT)

Description

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control. 6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.Subjects

data communication networks | data communication networks | architecture | architecture | network performance | network performance | network operation | network operation | next generation data networks | next generation data networks | network algorithms | network algorithms | mathematics | mathematics | probability theory | probability theory | layered network architecture | layered network architecture | Link Layer protocols | Link Layer protocols | high-speed packet switching | high-speed packet switching | queueing theory | queueing theory | Local Area Networks | Local Area Networks | Wide Area Networks | Wide Area Networks | 6.263 | 6.263 | 16.37 | 16.37License

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

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See all metadataBayesian Reasoning with Graphical Models Bayesian Reasoning with Graphical Models

Description

This course motivates and introduces graphical models (with special attention to Bayesian networks) as well consolidated and popular tools with the ability to represent knowledge under uncertainty and reason with it, one of the main challenges in building intelligent systems in Artificial Intelligence. Uncertainty is modelled with probability theory and reasoning is based on Bayes’ rule. Bayesian networks represent factorizations of joint probability distributions. Nodes represent the variables of the domain and links represent the properties of conditional dependences and independences among the variables. The course will provide an in-depth exposition of theoretical and practical underpinnings. The course starts explaining the meaning of these networks to model both causal and non-caus This course motivates and introduces graphical models (with special attention to Bayesian networks) as well consolidated and popular tools with the ability to represent knowledge under uncertainty and reason with it, one of the main challenges in building intelligent systems in Artificial Intelligence. Uncertainty is modelled with probability theory and reasoning is based on Bayes’ rule. Bayesian networks represent factorizations of joint probability distributions. Nodes represent the variables of the domain and links represent the properties of conditional dependences and independences among the variables. The course will provide an in-depth exposition of theoretical and practical underpinnings. The course starts explaining the meaning of these networks to model both causal and non-causSubjects

Ciencia de la Computación e Inteligencia Artificial | Ciencia de la Computación e Inteligencia Artificial | Computer Science and Artificial Intelligence | Computer Science and Artificial Intelligence | Networks Applications | Networks Applications | Redes Bayesianas | Redes Bayesianas | Bayesian Classifiers | Bayesian Classifiers | Graphical Models | Graphical Models | Bayesian Networks | Bayesian NetworksLicense

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See all metadataDescription

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution. This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.Subjects

Genomes: Biological sequence analysis | Genomes: Biological sequence analysis | hidden Markov models | hidden Markov models | gene finding | gene finding | RNA folding | RNA folding | sequence alignment | sequence alignment | genome assembly | genome assembly | Networks: Gene expression analysis | Networks: Gene expression analysis | regulatory motifs | regulatory motifs | graph algorithms | graph algorithms | scale-free networks | scale-free networks | network motifs | network motifs | network evolution | network evolution | Evolution: Comparative genomics | Evolution: Comparative genomics | phylogenetics | phylogenetics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | rapid evolution | rapid evolutionLicense

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

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See all metadataDescription

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theori The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theoriSubjects

Learning | Learning | Perspective | Perspective | Regularized | Regularized | Kernel Hilbert Spaces | Kernel Hilbert Spaces | Approximation | Approximation | Nonparametric | Nonparametric | Ridge Approximation | Ridge Approximation | Networks | Networks | Finance | Finance | Statistical Learning Theory | Statistical Learning Theory | Consistency | Consistency | Empirical Risk | Empirical Risk | Minimization Principle | Minimization Principle | VC-Dimension | VC-Dimension | VC-bounds | VC-bounds | Regression | Regression | Structural Risk Minimization | Structural Risk Minimization | Support Vector Machines | Support Vector Machines | Kernel Engineering | Kernel Engineering | Computer Vision | Computer Vision | Computer Graphics | Computer Graphics | Neuroscience | Neuroscience | Approximation Error | Approximation Error | Approximation Theory | Approximation Theory | Bioinformatics | Bioinformatics | Bagging | Bagging | Boosting | Boosting | Wavelets | Wavelets | Frames | FramesLicense

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

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See all metadata9.95-A Research Topics in Neuroscience (MIT) 9.95-A Research Topics in Neuroscience (MIT)

Description

This series of research talks by members of the Department of Brain and Cognitive Sciences introduces students to different approaches to the study of the brain and mind. Topics include: From Neurons to Neural Networks Prefrontal Cortex and the Neural Basis of Cognitive Control Hippocampal Memory Formation and the Role of Sleep The Formation of Internal Modes for Learning Motor Skills Look and See: How the Brain Selects Objects and Directs the Eyes How the Brain Wires Itself This series of research talks by members of the Department of Brain and Cognitive Sciences introduces students to different approaches to the study of the brain and mind. Topics include: From Neurons to Neural Networks Prefrontal Cortex and the Neural Basis of Cognitive Control Hippocampal Memory Formation and the Role of Sleep The Formation of Internal Modes for Learning Motor Skills Look and See: How the Brain Selects Objects and Directs the Eyes How the Brain Wires ItselfSubjects

Neurons | Neurons | Neural Networks | Neural Networks | Prefrontal Cortex | Prefrontal Cortex | Cognitive Control | Cognitive Control | Hippocampal Memory Formation | Hippocampal Memory Formation | Sleep | Sleep | Learning | Learning | Motor Skills | Motor Skills | Brain | Brain | Objects | Objects | Eye | Eye | Synapse | Synapse | organization | organizationLicense

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

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See all metadata6.263J Data Communication Networks (MIT)

Description

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.Subjects

data communication networks | architecture | network performance | network operation | next generation data networks | network algorithms | mathematics | probability theory | layered network architecture | Link Layer protocols | high-speed packet switching | queueing theory | Local Area Networks | Wide Area Networks | 6.263 | 16.37License

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

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See all metadata6.263J Data Communication Networks (MIT)

Description

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.Subjects

data communication networks | architecture | network performance | network operation | next generation data networks | network algorithms | mathematics | probability theory | layered network architecture | Link Layer protocols | high-speed packet switching | queueing theory | Local Area Networks | Wide Area Networks | 6.263 | 16.37License

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

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See all metadata6.263J Data Communication Networks (MIT)

Description

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.Subjects

data communication networks | architecture | network performance | network operation | next generation data networks | network algorithms | mathematics | probability theory | layered network architecture | Link Layer protocols | high-speed packet switching | queueing theory | Local Area Networks | Wide Area Networks | 6.263 | 16.37License

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

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See all metadata6.047 Computational Biology (MIT) 6.047 Computational Biology (MIT)

Description

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets. This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.Subjects

Genomes | Genomes | Networks | Networks | Evolution | Evolution | computational biology | computational biology | genomics | genomics | comparative genomics | comparative genomics | epigenomics | epigenomics | functional genomics | motifs | functional genomics | motifs | phylogenomics | phylogenomics | personal genomics | personal genomics | algorithms | algorithms | machine learning | machine learning | biology | biology | biological datasets | biological datasets | proteomics | proteomics | sequence analysis | sequence analysis | sequence alignment | sequence alignment | genome assembly | genome assembly | network motifs | network motifs | network evolution | network evolution | graph algorithms | graph algorithms | phylogenetics | phylogenetics | python | python | probability | probability | statistics | statistics | entropy | entropy | information | informationLicense

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

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See all metadata6.263J Data Communication Networks (MIT)

Description

6.263J / 16.37J focuses on the fundamentals of data communication networks. One goal is to give some insight into the rationale of why networks are structured the way they are today and to understand the issues facing the designers of next-generation data networks. Much of the course focuses on network algorithms and their performance. Students are expected to have a strong mathematical background and an understanding of probability theory. Topics discussed include: layered network architecture, Link Layer protocols, high-speed packet switching, queueing theory, Local Area Networks, and Wide Area Networking issues, including routing and flow control.Subjects

data communication networks | architecture | network performance | network operation | next generation data networks | network algorithms | mathematics | probability theory | layered network architecture | Link Layer protocols | high-speed packet switching | queueing theory | Local Area Networks | Wide Area Networks | 6.263 | 16.37License

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

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See all metadata9.520-A Networks for Learning: Regression and Classification (MIT)

Description

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theoriSubjects

Learning | Perspective | Regularized | Kernel Hilbert Spaces | Approximation | Nonparametric | Ridge Approximation | Networks | Finance | Statistical Learning Theory | Consistency | Empirical Risk | Minimization Principle | VC-Dimension | VC-bounds | Regression | Structural Risk Minimization | Support Vector Machines | Kernel Engineering | Computer Vision | Computer Graphics | Neuroscience | Approximation Error | Approximation Theory | Bioinformatics | Bagging | Boosting | Wavelets | FramesLicense

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

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See all metadata9.95-A Research Topics in Neuroscience (MIT)

Description

This series of research talks by members of the Department of Brain and Cognitive Sciences introduces students to different approaches to the study of the brain and mind. Topics include: From Neurons to Neural Networks Prefrontal Cortex and the Neural Basis of Cognitive Control Hippocampal Memory Formation and the Role of Sleep The Formation of Internal Modes for Learning Motor Skills Look and See: How the Brain Selects Objects and Directs the Eyes How the Brain Wires ItselfSubjects

Neurons | Neural Networks | Prefrontal Cortex | Cognitive Control | Hippocampal Memory Formation | Sleep | Learning | Motor Skills | Brain | Objects | Eye | Synapse | organizationLicense

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

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See all metadata6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.Subjects

Genomes: Biological sequence analysis | hidden Markov models | gene finding | RNA folding | sequence alignment | genome assembly | Networks: Gene expression analysis | regulatory motifs | graph algorithms | scale-free networks | network motifs | network evolution | Evolution: Comparative genomics | phylogenetics | genome duplication | genome rearrangements | evolutionary theory | rapid evolutionLicense

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

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See all metadata6.047 Computational Biology (MIT)

Description

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.Subjects

Genomes | Networks | Evolution | computational biology | genomics | comparative genomics | epigenomics | functional genomics | motifs | phylogenomics | personal genomics | algorithms | machine learning | biology | biological datasets | proteomics | sequence analysis | sequence alignment | genome assembly | network motifs | network evolution | graph algorithms | phylogenetics | python | probability | statistics | entropy | informationLicense

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

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See all metadata9.520-A Networks for Learning: Regression and Classification (MIT)

Description

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theoriSubjects

Learning | Perspective | Regularized | Kernel Hilbert Spaces | Approximation | Nonparametric | Ridge Approximation | Networks | Finance | Statistical Learning Theory | Consistency | Empirical Risk | Minimization Principle | VC-Dimension | VC-bounds | Regression | Structural Risk Minimization | Support Vector Machines | Kernel Engineering | Computer Vision | Computer Graphics | Neuroscience | Approximation Error | Approximation Theory | Bioinformatics | Bagging | Boosting | Wavelets | FramesLicense

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

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See all metadata9.95-A Research Topics in Neuroscience (MIT)

Description

This series of research talks by members of the Department of Brain and Cognitive Sciences introduces students to different approaches to the study of the brain and mind. Topics include: From Neurons to Neural Networks Prefrontal Cortex and the Neural Basis of Cognitive Control Hippocampal Memory Formation and the Role of Sleep The Formation of Internal Modes for Learning Motor Skills Look and See: How the Brain Selects Objects and Directs the Eyes How the Brain Wires ItselfSubjects

Neurons | Neural Networks | Prefrontal Cortex | Cognitive Control | Hippocampal Memory Formation | Sleep | Learning | Motor Skills | Brain | Objects | Eye | Synapse | organizationLicense

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

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See all metadata6.881 Computational Personal Genomics: Making Sense of Complete Genomes (MIT)

Description

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.Subjects

Genomes | Networks | Evolution | computational biology | genomics | comparative genomics | epigenomics | functional genomics | motifs | phylogenomics | personal genomics | algorithms | machine learning | biology | biological datasets | proteomics | sequence analysis | sequence alignment | genome assembly | network motifs | network evolution | graph algorithms | phylogenetics | python | probability | statistics | entropy | informationLicense

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

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See all metadataP00503 Secure Programming: Examination Paper

Description

Examination paperSubjects

Faculty of Technology | Design and Environment\P005 Networks (Computing)\P00503 Secure ProgrammingLicense

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