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21L.448J Darwin and Design (MIT) 21L.448J Darwin and Design (MIT)

Description

In the Origin of Species (1859), Charles Darwin gave us a model for understanding how natural objects and systems can evidence design without positing a designer: how purpose and mechanism can exist without intelligent agency. Texts in this course deal with pre- and post-Darwinian treatment of this topic within literature and speculative thought since the eighteenth century. We will give some attention to the modern study of feedback mechanisms in artificial intelligence. Our reading will be in Hume, Voltaire, Malthus, Darwin, Butler, H. G. Wells, and Turing. In the Origin of Species (1859), Charles Darwin gave us a model for understanding how natural objects and systems can evidence design without positing a designer: how purpose and mechanism can exist without intelligent agency. Texts in this course deal with pre- and post-Darwinian treatment of this topic within literature and speculative thought since the eighteenth century. We will give some attention to the modern study of feedback mechanisms in artificial intelligence. Our reading will be in Hume, Voltaire, Malthus, Darwin, Butler, H. G. Wells, and Turing.

Subjects

Origin of Species | Origin of Species | Darwin | Darwin | intelligent agency | intelligent agency | literature | literature | speculative thought | speculative thought | eighteenth century | eighteenth century | feedback mechanism | feedback mechanism | artificial intelligence | artificial intelligence | Hume | Hume | Voltaire | Voltaire | Malthus | Malthus | Butler | Butler | Hardy | Hardy | H.G. Wells | H.G. Wells | Freud | Freud | Evolution | Evolution | Modern Western philosophy | Modern Western philosophy | Philosophy of science | Philosophy of science | Religion | Religion | Science | Science | Life Sciences | Life Sciences | Social Aspects | Social Aspects | History | History | Intelligent design | individual species | Intelligent design | individual species | complexity | complexity | development | development | God theory of evolution | God theory of evolution | science | science | theological explanation | theological explanation | universe | universe | creatures | creatures | faith | faith | and theology | and theology | purpose of evolution | purpose of evolution | Design | Design | models | models | adaptation | adaptation

License

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6.895 Computational Biology: Genomes, Networks, Evolution (MIT) 6.895 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. 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 evolution

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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8.08 Statistical Physics II (MIT) 8.08 Statistical Physics II (MIT)

Description

Probability distributions for classical and quantum systems. Microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials. Conditions of thermodynamic equilibrium for homogenous and heterogenous systems. Applications: non-interacting Bose and Fermi gases; mean field theories for real gases, binary mixtures, magnetic systems, polymer solutions; phase and reaction equilibria, critical phenomena. Fluctuations, correlation functions and susceptibilities, and Kubo formulae. Evolution of distribution functions: Boltzmann and Smoluchowski equations. Probability distributions for classical and quantum systems. Microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials. Conditions of thermodynamic equilibrium for homogenous and heterogenous systems. Applications: non-interacting Bose and Fermi gases; mean field theories for real gases, binary mixtures, magnetic systems, polymer solutions; phase and reaction equilibria, critical phenomena. Fluctuations, correlation functions and susceptibilities, and Kubo formulae. Evolution of distribution functions: Boltzmann and Smoluchowski equations.

Subjects

Probability distributions | Probability distributions | quantum systems | quantum systems | Microcanonical | Microcanonical | canonical | canonical | grand canonical partition-functions | grand canonical partition-functions | thermodynamic potentials | thermodynamic potentials | Conditions of thermodynamic equilibrium for homogenous and heterogenous systems | Conditions of thermodynamic equilibrium for homogenous and heterogenous systems | non-interacting Bose and Fermi gases | non-interacting Bose and Fermi gases | mean field theories for real gases | mean field theories for real gases | binary mixtures | binary mixtures | magnetic systems | magnetic systems | polymer solutions | polymer solutions | phase and reaction equilibria | phase and reaction equilibria | critical phenomena | critical phenomena | Fluctuations | Fluctuations | correlation functions and susceptibilities | correlation functions and susceptibilities | Kubo formulae | Kubo formulae | Evolution of distribution functions | Evolution of distribution functions | Boltzmann and Smoluchowski equations | Boltzmann and Smoluchowski equations | correlation functions | correlation functions | susceptibilities | susceptibilities

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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21L.448J Darwin and Design (MIT) 21L.448J Darwin and Design (MIT)

Description

Includes audio/video content: AV lectures. Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology. Includes audio/video content: AV lectures. Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.

Subjects

21L.448 | 21L.448 | 21W.739 | 21W.739 | Origin of Species | Origin of Species | Darwin | Darwin | intelligent agency | intelligent agency | literature | literature | speculative thought | speculative thought | eighteenth century | eighteenth century | feedback mechanism | feedback mechanism | artificial intelligence | artificial intelligence | Hume | Hume | Voltaire | Voltaire | Malthus | Malthus | Butler | Butler | Hardy | Hardy | H.G. Wells | H.G. Wells | Freud | Freud | Evolution | Evolution | Modern Western philosophy | Modern Western philosophy | Philosophy of science | Philosophy of science | Religion | Religion | Science | Science | Life Sciences | Life Sciences | Social Aspects | Social Aspects | History | History | Intelligent design | individual species | Intelligent design | individual species | complexity | complexity | development | development | God theory of evolution | God theory of evolution | science | science | theological explanation | theological explanation | universe | universe | creatures | creatures | faith | faith | and theology | and theology | purpose of evolution | purpose of evolution | Design | Design | models | models | adaptation | adaptation

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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8.08 Statistical Physics II (MIT) 8.08 Statistical Physics II (MIT)

Description

This course covers probability distributions for classical and quantum systems. Topics include: Microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials. Also discussed are conditions of thermodynamic equilibrium for homogenous and heterogenous systems. The course follows 8.044, Statistical Physics I, and is second in this series of undergraduate Statistical Physics courses. This course covers probability distributions for classical and quantum systems. Topics include: Microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials. Also discussed are conditions of thermodynamic equilibrium for homogenous and heterogenous systems. The course follows 8.044, Statistical Physics I, and is second in this series of undergraduate Statistical Physics courses.

Subjects

Probability distributions | Probability distributions | quantum systems | quantum systems | Microcanonical | canonical | and grand canonical partition-functions | Microcanonical | canonical | and grand canonical partition-functions | thermodynamic potentials | thermodynamic potentials | Conditions of thermodynamic equilibrium for homogenous and heterogenous systems | Conditions of thermodynamic equilibrium for homogenous and heterogenous systems | non-interacting Bose and Fermi gases | non-interacting Bose and Fermi gases | mean field theories for real gases | mean field theories for real gases | binary mixtures | binary mixtures | magnetic systems | magnetic systems | polymer solutions | polymer solutions | phase and reaction equilibria | phase and reaction equilibria | critical phenomena | critical phenomena | Fluctuations | Fluctuations | correlation functions and susceptibilities | and Kubo formulae | correlation functions and susceptibilities | and Kubo formulae | Evolution of distribution functions: Boltzmann and Smoluchowski equations | Evolution of distribution functions: Boltzmann and Smoluchowski equations

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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12.517 Dynamics of Complex Systems: Biological and Environmental Coevolution Preceding the Cambrian Explosion (MIT) 12.517 Dynamics of Complex Systems: Biological and Environmental Coevolution Preceding the Cambrian Explosion (MIT)

Description

This seminar will focus on dynamical change in biogeochemical cycles accompanying early animal evolution -- beginning with the time of the earliest known microscopic animal fossils (~600 million years ago) and culminating (~100 million years later) with the rapid diversification of marine animals known as the "Cambrian explosion." Recent work indicates that this period of intense biological evolution was both a cause and an effect of changes in global biogeochemical cycles. We will seek to identify and quantify such coevolutionary changes. Lectures and discussions will attempt to unite the perspectives of quantitative theory, organic geochemistry, and evolutionary biology. This seminar will focus on dynamical change in biogeochemical cycles accompanying early animal evolution -- beginning with the time of the earliest known microscopic animal fossils (~600 million years ago) and culminating (~100 million years later) with the rapid diversification of marine animals known as the "Cambrian explosion." Recent work indicates that this period of intense biological evolution was both a cause and an effect of changes in global biogeochemical cycles. We will seek to identify and quantify such coevolutionary changes. Lectures and discussions will attempt to unite the perspectives of quantitative theory, organic geochemistry, and evolutionary biology.

Subjects

Evolution | Evolution | fossils | fossils | Cambrian explosion | Cambrian explosion | global biogeochemical cycles | global biogeochemical cycles | geobiology | geobiology | coevolution | coevolution | quantitative theory | quantitative theory | organic geochemistry | organic geochemistry | evolutionary biology | evolutionary biology | marine animals | marine animals

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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18.413 Error-Correcting Codes Laboratory (MIT) 18.413 Error-Correcting Codes Laboratory (MIT)

Description

This course introduces students to iterative decoding algorithms and the codes to which they are applied, including Turbo Codes, Low-Density Parity-Check Codes, and Serially-Concatenated Codes. The course will begin with an introduction to the fundamental problems of Coding Theory and their mathematical formulations. This will be followed by a study of Belief Propagation--the probabilistic heuristic which underlies iterative decoding algorithms. Belief Propagation will then be applied to the decoding of Turbo, LDPC, and Serially-Concatenated codes. The technical portion of the course will conclude with a study of tools for explaining and predicting the behavior of iterative decoding algorithms, including EXIT charts and Density Evolution. This course introduces students to iterative decoding algorithms and the codes to which they are applied, including Turbo Codes, Low-Density Parity-Check Codes, and Serially-Concatenated Codes. The course will begin with an introduction to the fundamental problems of Coding Theory and their mathematical formulations. This will be followed by a study of Belief Propagation--the probabilistic heuristic which underlies iterative decoding algorithms. Belief Propagation will then be applied to the decoding of Turbo, LDPC, and Serially-Concatenated codes. The technical portion of the course will conclude with a study of tools for explaining and predicting the behavior of iterative decoding algorithms, including EXIT charts and Density Evolution.

Subjects

iterative decoding | iterative decoding | error-correcting codes | error-correcting codes | Turbo Codes | Turbo Codes | Low-Density Parity-Check Codes | Low-Density Parity-Check Codes | serially concatenated codes | serially concatenated codes | aid code design | aid code design | iterative decoding algorithms | iterative decoding algorithms | Belief Propagation Serially-Concatenated codes | Belief Propagation Serially-Concatenated codes | EXIT charts | EXIT charts | Density Evolution | Density Evolution

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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Charles Darwin lectures at the University of Nottingham Charles Darwin lectures at the University of Nottingham

Description

As part of the University of Nottingham, School of Biology's 200 years of Darwin celebrations, Darwin — aka evolutionary geneticist Professor John Brookfield in full Victorian attire — outlines the ideas from his 1859 breakthrough publication The Origin of Species, which presented the theory of natural selection as the main driving force for evolution. Presentation delivered March 2009 Suitable for Undergraduate study and community education Professor John Brookfield, Professor of Evolutionary Genetics, School of Biology Professor John Brookfield has a BA in Zoology, University of Oxford 1976; PhD in Population Genetics, University of London 1980; He has worked as a Research Demonstrator in Genetics, University College of Swansea 1979-1981; Visiting Fellow, Laboratory of Genetics As part of the University of Nottingham, School of Biology's 200 years of Darwin celebrations, Darwin — aka evolutionary geneticist Professor John Brookfield in full Victorian attire — outlines the ideas from his 1859 breakthrough publication The Origin of Species, which presented the theory of natural selection as the main driving force for evolution. Presentation delivered March 2009 Suitable for Undergraduate study and community education Professor John Brookfield, Professor of Evolutionary Genetics, School of Biology Professor John Brookfield has a BA in Zoology, University of Oxford 1976; PhD in Population Genetics, University of London 1980; He has worked as a Research Demonstrator in Genetics, University College of Swansea 1979-1981; Visiting Fellow, Laboratory of Genetics

Subjects

UNow | UNow | Evolution | Evolution | Science | Science | Biology | Biology | Genetics | Genetics | Darwin | Darwin | UKOER | UKOER

License

Except for third party materials (materials owned by someone other than The University of Nottingham) and where otherwise indicated, the copyright in the content provided in this resource is owned by The University of Nottingham and licensed under a Creative Commons Attribution-NonCommercial-ShareAlike UK 2.0 Licence (BY-NC-SA) Except for third party materials (materials owned by someone other than The University of Nottingham) and where otherwise indicated, the copyright in the content provided in this resource is owned by The University of Nottingham and licensed under a Creative Commons Attribution-NonCommercial-ShareAlike UK 2.0 Licence (BY-NC-SA)

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Darwin for a day Darwin for a day

Description

Professor John Brookfield has a BA in Zoology, University of Oxford 1976; PhD in Population Genetics, University of London 1980; He has worked as a Research Demonstrator in Genetics, University College of Swansea 1979-1981; Visiting Fellow, Laboratory of Genetics, The National Institute of Environmental Health Sciences, North Carolina 1981-1983; Lecturer in Genetics, University of Leicester 1983-1986; Lecturer (1987), Reader (1997) and Professor of Evolutionary Genetics (2004) University of Nottingham. He was Managing Editor, Heredity (2000-2003). Vice-President (External Affairs), Genetics Society 2008-, Appointed Fellow of the Institute of Biology, 2009. Member RAE Biological Sciences Panel and Sub-Panel, 2001 and 2008. Professor John Brookfield has a BA in Zoology, University of Oxford 1976; PhD in Population Genetics, University of London 1980; He has worked as a Research Demonstrator in Genetics, University College of Swansea 1979-1981; Visiting Fellow, Laboratory of Genetics, The National Institute of Environmental Health Sciences, North Carolina 1981-1983; Lecturer in Genetics, University of Leicester 1983-1986; Lecturer (1987), Reader (1997) and Professor of Evolutionary Genetics (2004) University of Nottingham. He was Managing Editor, Heredity (2000-2003). Vice-President (External Affairs), Genetics Society 2008-, Appointed Fellow of the Institute of Biology, 2009. Member RAE Biological Sciences Panel and Sub-Panel, 2001 and 2008. As part of the University of Nottingham, School of Biology's 200 years of Darwin celebrations, evolutionary geneticist Professor John Brookfield in full Victorian attire delivered a talk, as Darwin, on the theory of evolution via natural selection. In this video Professor John Brookfield is interviewed about his experience of being Darwin for a day Interview took place March 2009 Suitable for Undergraduate study and community education Professor John Brookfield, Professor of Evolutionary Genetics, School of Biology Professor John Brookfield has a BA in Zoology, University of Oxford 1976; PhD in Population Genetics, University of London 1980; He has worked as a Research Demonstrator in Genetics, University College of Swansea 1979-1981; Visiting Fellow, Laboratory of Genetics, The Na As part of the University of Nottingham, School of Biology's 200 years of Darwin celebrations, evolutionary geneticist Professor John Brookfield in full Victorian attire delivered a talk, as Darwin, on the theory of evolution via natural selection. In this video Professor John Brookfield is interviewed about his experience of being Darwin for a day Interview took place March 2009 Suitable for Undergraduate study and community education Professor John Brookfield, Professor of Evolutionary Genetics, School of Biology Professor John Brookfield has a BA in Zoology, University of Oxford 1976; PhD in Population Genetics, University of London 1980; He has worked as a Research Demonstrator in Genetics, University College of Swansea 1979-1981; Visiting Fellow, Laboratory of Genetics, The Na

Subjects

UNow | UNow | Evolution | Evolution | Science | Science | Biology | Biology | Genetics | Genetics | Darwin | Darwin | UKOER | UKOER

License

Except for third party materials (materials owned by someone other than The University of Nottingham) and where otherwise indicated, the copyright in the content provided in this resource is owned by The University of Nottingham and licensed under a Creative Commons Attribution-NonCommercial-ShareAlike UK 2.0 Licence (BY-NC-SA) Except for third party materials (materials owned by someone other than The University of Nottingham) and where otherwise indicated, the copyright in the content provided in this resource is owned by The University of Nottingham and licensed under a Creative Commons Attribution-NonCommercial-ShareAlike UK 2.0 Licence (BY-NC-SA)

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6.047 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 | information

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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An analysis of the impact of the Gaia Theory on Ecology and Evolutionary Theory

Description

This paper investigates the impact of ideas published within the Gaia theory (as set out by James Lovelock in 1979), on the study of Ecology and Evolutionary Theory. Developments within both disciplines have been influenced, and shaped by the Gaia theory and the paper discusses these. The development of the Daisyworld model, which highlighted for ecologists the importance of interactions within an ecosystem between the biota and the abiotic world, contributed to the understanding of biodiversity. The Gaia theory also predicted the causal link between increased biodiversity and increasing stability of populations. The Gaian influence on the development of Evolutionary theory can be found in the idea that life on earth works with the abiotic environment as a self-regulatory system. This idea

Subjects

Gaia Theory | Ecology | Biodiversity | Evolutionary Theory | Lovelock | Dawkins

License

copyright Oxford Brookes University, except where indicated in the item description. Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 UK: England & Wales License. copyright Oxford Brookes University, except where indicated in the item description. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 UK: England & Wales License.

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21L.448J Darwin and Design (MIT)

Description

In the Origin of Species (1859), Charles Darwin gave us a model for understanding how natural objects and systems can evidence design without positing a designer: how purpose and mechanism can exist without intelligent agency. Texts in this course deal with pre- and post-Darwinian treatment of this topic within literature and speculative thought since the eighteenth century. We will give some attention to the modern study of feedback mechanisms in artificial intelligence. Our reading will be in Hume, Voltaire, Malthus, Darwin, Butler, H. G. Wells, and Turing.

Subjects

Origin of Species | Darwin | intelligent agency | literature | speculative thought | eighteenth century | feedback mechanism | artificial intelligence | Hume | Voltaire | Malthus | Butler | Hardy | H.G. Wells | Freud | Evolution | Modern Western philosophy | Philosophy of science | Religion | Science | Life Sciences | Social Aspects | History | Intelligent design | individual species | complexity | development | God theory of evolution | science | theological explanation | universe | creatures | faith | and theology | purpose of evolution | Design | models | adaptation

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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6.895 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 evolution

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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8.08 Statistical Physics II (MIT)

Description

Probability distributions for classical and quantum systems. Microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials. Conditions of thermodynamic equilibrium for homogenous and heterogenous systems. Applications: non-interacting Bose and Fermi gases; mean field theories for real gases, binary mixtures, magnetic systems, polymer solutions; phase and reaction equilibria, critical phenomena. Fluctuations, correlation functions and susceptibilities, and Kubo formulae. Evolution of distribution functions: Boltzmann and Smoluchowski equations.

Subjects

Probability distributions | quantum systems | Microcanonical | canonical | grand canonical partition-functions | thermodynamic potentials | Conditions of thermodynamic equilibrium for homogenous and heterogenous systems | non-interacting Bose and Fermi gases | mean field theories for real gases | binary mixtures | magnetic systems | polymer solutions | phase and reaction equilibria | critical phenomena | Fluctuations | correlation functions and susceptibilities | Kubo formulae | Evolution of distribution functions | Boltzmann and Smoluchowski equations | correlation functions | susceptibilities

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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8.08 Statistical Physics II (MIT)

Description

This course covers probability distributions for classical and quantum systems. Topics include: Microcanonical, canonical, and grand canonical partition-functions and associated thermodynamic potentials. Also discussed are conditions of thermodynamic equilibrium for homogenous and heterogenous systems. The course follows 8.044, Statistical Physics I, and is second in this series of undergraduate Statistical Physics courses.

Subjects

Probability distributions | quantum systems | Microcanonical | canonical | and grand canonical partition-functions | thermodynamic potentials | Conditions of thermodynamic equilibrium for homogenous and heterogenous systems | non-interacting Bose and Fermi gases | mean field theories for real gases | binary mixtures | magnetic systems | polymer solutions | phase and reaction equilibria | critical phenomena | Fluctuations | correlation functions and susceptibilities | and Kubo formulae | Evolution of distribution functions: Boltzmann and Smoluchowski equations

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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18.413 Error-Correcting Codes Laboratory (MIT)

Description

This course introduces students to iterative decoding algorithms and the codes to which they are applied, including Turbo Codes, Low-Density Parity-Check Codes, and Serially-Concatenated Codes. The course will begin with an introduction to the fundamental problems of Coding Theory and their mathematical formulations. This will be followed by a study of Belief Propagation--the probabilistic heuristic which underlies iterative decoding algorithms. Belief Propagation will then be applied to the decoding of Turbo, LDPC, and Serially-Concatenated codes. The technical portion of the course will conclude with a study of tools for explaining and predicting the behavior of iterative decoding algorithms, including EXIT charts and Density Evolution.

Subjects

iterative decoding | error-correcting codes | Turbo Codes | Low-Density Parity-Check Codes | serially concatenated codes | aid code design | iterative decoding algorithms | Belief Propagation Serially-Concatenated codes | EXIT charts | Density Evolution

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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21L.448J Darwin and Design (MIT)

Description

Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.

Subjects

21L.448 | 21W.739 | Origin of Species | Darwin | intelligent agency | literature | speculative thought | eighteenth century | feedback mechanism | artificial intelligence | Hume | Voltaire | Malthus | Butler | Hardy | H.G. Wells | Freud | Evolution | Modern Western philosophy | Philosophy of science | Religion | Science | Life Sciences | Social Aspects | History | Intelligent design | individual species | complexity | development | God theory of evolution | science | theological explanation | universe | creatures | faith | and theology | purpose of evolution | Design | models | adaptation

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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6.047 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 | information

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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12.517 Dynamics of Complex Systems: Biological and Environmental Coevolution Preceding the Cambrian Explosion (MIT)

Description

This seminar will focus on dynamical change in biogeochemical cycles accompanying early animal evolution -- beginning with the time of the earliest known microscopic animal fossils (~600 million years ago) and culminating (~100 million years later) with the rapid diversification of marine animals known as the "Cambrian explosion." Recent work indicates that this period of intense biological evolution was both a cause and an effect of changes in global biogeochemical cycles. We will seek to identify and quantify such coevolutionary changes. Lectures and discussions will attempt to unite the perspectives of quantitative theory, organic geochemistry, and evolutionary biology.

Subjects

Evolution | fossils | Cambrian explosion | global biogeochemical cycles | geobiology | coevolution | quantitative theory | organic geochemistry | evolutionary biology | marine animals

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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6.881 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 | information

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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Investigating the Structure, Function and Evolution of the External Genital Structures of Drosophila Species

Description

Drosophila male genitalia exhibit incredible divergent morphology,representing one of the most striking examples of morphological evolution [1]. To understand the rapid evolution of these traits, ongoing research aims to characterize the genetic architecture underlying differences in genital morphology [2]. Sex-specific behaviours have been shown to potentially originate from differences in brain structure [3]. In most cases gene expression is restricted to small groups of neurons; this would potentially provide a starting point for circuit identification [3]. Focusing on how individual neurons or subsets of neurons contribute to the development and function of neuronal networks would provide an important perspective of the ways in which Drosopholids have evolved, and how this affects cert

Subjects

Evolution | male genitalia | Drosophila | divergence | development | Minos-mediated integration cassette (MiMIC)

License

https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/

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