Description

6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.  6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered. 

Subjects

machine learning | machine learning | perceptrons | perceptrons | boosting | boosting | support vector machines | support vector machines | Markov | Markov | hidden Markov models | hidden Markov models | HMM | HMM | Bayesian networks | Bayesian networks | statistical inference | statistical inference | regression | regression | clustering | clustering | bias | bias | variance | variance | regularization | regularization | Generalized Linear Models | Generalized Linear Models | neural networks | neural networks | Support Vector Machine | Support Vector Machine | SVM | SVM | mixture models | mixture models | kernel density estimation | kernel density estimation | gradient descent | gradient descent | quadratic programming | quadratic programming | EM algorithm | EM algorithm | orward-backward algorithm | orward-backward algorithm | junction tree algorithm | junction tree algorithm | Gibbs sampling | Gibbs sampling

License

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

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This course is an introduction to many of the central issues in a branch of philosophy called philosophy of mind. Some of the questions we will discuss include the following. Can computers think? Is the mind an immaterial thing? Or is the mind the brain? Or does the mind stand to the brain as a computer program stands to the hardware? How can creatures like ourselves think thoughts that are "about" things? (For example, we can all think that Aristotle is a philosopher, and in that sense think "about" Aristotle, but what is the explanation of this quite remarkable ability?) Can I know whether your experiences and my experiences when we look at raspberries, fire trucks and stop lights are the same? Can consciousness be given a scientific explanation? This course is an introduction to many of the central issues in a branch of philosophy called philosophy of mind. Some of the questions we will discuss include the following. Can computers think? Is the mind an immaterial thing? Or is the mind the brain? Or does the mind stand to the brain as a computer program stands to the hardware? How can creatures like ourselves think thoughts that are "about" things? (For example, we can all think that Aristotle is a philosopher, and in that sense think "about" Aristotle, but what is the explanation of this quite remarkable ability?) Can I know whether your experiences and my experiences when we look at raspberries, fire trucks and stop lights are the same? Can consciousness be given a scientific explanation?

Subjects

artificial intelligence | artificial intelligence | psychology | psychology | philosophy | philosophy | Turing Machines | Turing Machines | consciousness | consciousness | computer limitations | computer limitations | computation | computation | neurophysiology | neurophysiology | Turing test | Turing test | the analog/digital distinction | the analog/digital distinction | Chinese Room argument | Chinese Room argument | causal efficacy of content | causal efficacy of content | inverted spectrum | inverted spectrum | mental representation | mental representation | procedural semantics | procedural semantics | connectionism | connectionism

License

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

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This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses

Subjects

supervised learning | supervised learning | statistical learning | statistical learning | multivariate function | multivariate function | Support Vector Machines | Support Vector Machines | regression | regression | classification | classification | VC theory | VC theory | computer vision | computer vision | computer graphics | computer graphics | bioinformatics | bioinformatics

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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Survey and special topics designed for students in Brain and Cognitive Sciences. Emphasizes ethological studies of natural behavior patterns and their analysis in laboratory work, with contributions from field biology (mammology, primatology), sociobiology, and comparative psychology. Stresses human behavior but also includes major contributions from studies of other animals. Survey and special topics designed for students in Brain and Cognitive Sciences. Emphasizes ethological studies of natural behavior patterns and their analysis in laboratory work, with contributions from field biology (mammology, primatology), sociobiology, and comparative psychology. Stresses human behavior but also includes major contributions from studies of other animals.

Subjects

Behavioral modification | Behavioral modification | ethology | ethology | sociobiology | sociobiology | learning | learning | Social Status | Social Status | Cross-Cultural Differences | Cross-Cultural Differences | Persuasion | Persuasion | Politics | Politics | Individual | Individual | Sexuality | Sexuality | Dimorphisms in body and behavior | Dimorphisms in body and behavior | social organization | social organization | dominance structures | dominance structures | evolution of sexual signals | evolution of sexual signals | emancipation | emancipation | Mating | Mating | reproduction | reproduction | Emotion | Emotion | Facial Expression | Facial Expression | Displays | Displays | General Non-Verbal Communication | General Non-Verbal Communication | Sex Modeling behaviors | Sex Modeling behaviors | Machine interfaces | Machine interfaces | Cognitive ethology | Cognitive ethology | Comparative cognition | Comparative cognition | Signs | Signs | Symbols | Symbols | pharmacology | pharmacology

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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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 theori

Subjects

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 | Frames

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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Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject. Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Subjects

supervised learning | supervised learning | statistical learning | statistical learning | multivariate function | multivariate function | Support Vector Machines | Support Vector Machines | regression | regression | classification | classification | VC theory | VC theory | computer vision | computer vision | computer graphics | computer graphics | bioinformatics | bioinformatics

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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In analyzing fiscal issues, conventional public finance approaches focus mainly on taxation and public spending. Policymakers and practitioners rarely explore solutions by examining the fundamental problem: the failure of interested parties to act collectively to internalize the positive externalities generated by public goods. Public finance is merely one of many possible institutional arrangements for assigning the rights and responsibilities to public goods consumption. This system is currently under stress because of the financial crisis. The first part of the class will focus on collective action and its connection with local public finance. The second part will explore alternative institutional arrangements for mediating collective action problems associated with the provision of loc In analyzing fiscal issues, conventional public finance approaches focus mainly on taxation and public spending. Policymakers and practitioners rarely explore solutions by examining the fundamental problem: the failure of interested parties to act collectively to internalize the positive externalities generated by public goods. Public finance is merely one of many possible institutional arrangements for assigning the rights and responsibilities to public goods consumption. This system is currently under stress because of the financial crisis. The first part of the class will focus on collective action and its connection with local public finance. The second part will explore alternative institutional arrangements for mediating collective action problems associated with the provision of loc

Subjects

Advanced | Advanced | Urban | Urban | Public Finance | Public Finance | Collective Action and Provisions of Local Public Goods | Collective Action and Provisions of Local Public Goods | Machine | Machine | Technology | Technology | Globalization | Globalization | Cities | Cities | Culturing Life | Culturing Life | Economic Reform | Economic Reform | Public Policy | Public Policy | Education | Education | Social Movement | Social Movement | Current Crises | Current Crises | Nation | Nation | Economy | Economy | Social Science Analysis | Social Science Analysis | Social Reform | Social Reform | Economic Data | Economic Data | Suburban | Suburban | Neighborhood Composition | Neighborhood Composition | Infrastructure Development | Infrastructure Development | Changing Federal Policies | Changing Federal Policies | Wealth Transfer | Wealth Transfer | Social Groups | Social Groups | Data | Data

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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Survey and special topics designed for students in Brain and Cognitive Sciences. Emphasizes ethological studies of natural behavior patterns and their analysis in laboratory work, with contributions from field biology (mammology, primatology), sociobiology, and comparative psychology. Stresses human behavior but also includes major contributions from studies of other animals. Survey and special topics designed for students in Brain and Cognitive Sciences. Emphasizes ethological studies of natural behavior patterns and their analysis in laboratory work, with contributions from field biology (mammology, primatology), sociobiology, and comparative psychology. Stresses human behavior but also includes major contributions from studies of other animals.

Subjects

Behavioral modification | Behavioral modification | ethology | ethology | sociobiology | sociobiology | learning | learning | Social Status | Social Status | Cross-Cultural Differences | Cross-Cultural Differences | Persuasion | Persuasion | Politics | Politics | Individual | Individual | Sexuality | Sexuality | Dimorphisms in body and behavior | Dimorphisms in body and behavior | social organization | social organization | dominance structures | dominance structures | evolution of sexual signals | evolution of sexual signals | emancipation | emancipation | Mating | Mating | reproduction | reproduction | Emotion | Emotion | Facial Expression | Facial Expression | Displays | Displays | General Non-Verbal Communication | General Non-Verbal Communication | Sex Modeling behaviors | Sex Modeling behaviors | Machine interfaces | Machine interfaces | Cognitive ethology | Cognitive ethology | Comparative cognition | Comparative cognition | Signs | Signs | Symbols | Symbols | pharmacology | pharmacology

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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Francis Henry Dent was born at Holyhead on New Year’s Eve 1866, where his father, Admiral Charles Bayley Calmady Dent, was employed as the Marine Superintendent of the London & North-Western Railway. At the age of seventeen the young Francis … Continue reading → Francis Henry Dent was born at Holyhead on New Year’s Eve 1866, where his father, Admiral Charles Bayley Calmady Dent, was employed as the Marine Superintendent of the London & North-Western Railway. At the age of seventeen the young Francis … Continue reading →

Subjects

Machine | Machine | Unconventional Soldiers | Unconventional Soldiers | 355 (Military science) | 355 (Military science) | 358 (Other specialized forces & services) | 358 (Other specialized forces & services) | 610 (Medical Sciences) | 610 (Medical Sciences) | U (Military Science) | U (Military Science) | UA (Armies: Organization ? distribution ? military situation) | UA (Armies: Organization ? distribution ? military situation) | ww1 | ww1 | 358 (Other specialized forces & services) | 358 (Other specialized forces & services)

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The term ‘citizen soldier’ evokes a particularly powerful image in Britain. The poignant histories of the ‘Pals’ Battalions’, raised utilizing the attraction of geographical and occupational connections, have contributed greatly to the lasting public impression of the conflict. Names such … Continue reading → The term ‘citizen soldier’ evokes a particularly powerful image in Britain. The poignant histories of the ‘Pals’ Battalions’, raised utilizing the attraction of geographical and occupational connections, have contributed greatly to the lasting public impression of the conflict. Names such … Continue reading →

Subjects

Machine | Machine | Unconventional Soldiers | Unconventional Soldiers | 355 (Military science) | 355 (Military science) | 358 (Other specialized forces & services) | 358 (Other specialized forces & services) | 900 (History & geography) | 900 (History & geography) | U (Military Science) | U (Military Science) | UA (Armies: Organization - distribution - military situation) | UA (Armies: Organization - distribution - military situation) | UG (Military engineering. Air forces) | UG (Military engineering. Air forces) | 358 (Other specialized forces & services) | 358 (Other specialized forces & services) | 900 (History & geography) | 900 (History & geography)

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In 1917, the American journalist Isaac Frederick Marcosson visited the General Headquarters [GHQ] of the British Expeditionary Force [BEF] at Montreuil-sur-Mer following a tour of the Western Front. He recorded his experiences of this visit for Everybody’s Magazine and in … Continue reading → In 1917, the American journalist Isaac Frederick Marcosson visited the General Headquarters [GHQ] of the British Expeditionary Force [BEF] at Montreuil-sur-Mer following a tour of the Western Front. He recorded his experiences of this visit for Everybody’s Magazine and in … Continue reading →

Subjects

Machine | Machine | 355 (Military science) | 355 (Military science) | 356 (Foot forces & warfare) | 356 (Foot forces & warfare) | 900 (History & geography) | 900 (History & geography) | 940 (General history of Europe) | 940 (General history of Europe) | Blogs | Blogs | U (Military Science) | U (Military Science) | UA (Armies: Organization - distribution - military situation) | UA (Armies: Organization - distribution - military situation) | 356 (Foot forces & warfare) | 356 (Foot forces & warfare) | 900 (History & geography) | 900 (History & geography)

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This course addresses the evolution of the modern capitalist economy and evaluates its current structure and performance. Various paradigms of economics are contrasted and compared (neoclassical, Marxist, socioeconomic, and neocorporate) in order to understand how modern capitalism has been shaped and how it functions in today's economy. The course stresses general analytic reasoning and problem formulation rather than specific analytic techniques. Readings include classics in economic thought as well as contemporary analyses. This course addresses the evolution of the modern capitalist economy and evaluates its current structure and performance. Various paradigms of economics are contrasted and compared (neoclassical, Marxist, socioeconomic, and neocorporate) in order to understand how modern capitalism has been shaped and how it functions in today's economy. The course stresses general analytic reasoning and problem formulation rather than specific analytic techniques. Readings include classics in economic thought as well as contemporary analyses.

Subjects

capitalism | capitalism | markets | markets | Thomas Kuhn | Thomas Kuhn | scientific paradigm | scientific paradigm | liberalism | liberalism | neoclassical economics | neoclassical economics | Marxism | Marxism | corporate state | corporate state | social embeddedness | social embeddedness | economic activity | economic activity | The Fountainhead | The Fountainhead | Ayn Rand | Ayn Rand | Double Helix | Double Helix | James Watson | James Watson | Tracy Kidder | Tracy Kidder | Soul of the New Machine | Soul of the New Machine | industrial state | industrial state | individualism | individualism

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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Basic course on Automata Theory and Formal Languages ??aimed at students of the Computer Science Degree . The common competences of the course to Computer Science are: Knowledge and application of basic algorithms and procedures of Computer Science to design solutions to problems, and analyze the suitability and complexity of the proposed algorithms; Knowledge and application of basic techniques and principles of intelligent systems and their practical application. Basic course on Automata Theory and Formal Languages ??aimed at students of the Computer Science Degree . The common competences of the course to Computer Science are: Knowledge and application of basic algorithms and procedures of Computer Science to design solutions to problems, and analyze the suitability and complexity of the proposed algorithms; Knowledge and application of basic techniques and principles of intelligent systems and their practical application.

Subjects

Finite Automata | Finite Automata | Lenguajes y Sistemas Informaticos | Lenguajes y Sistemas Informaticos | Regular Expressions | Regular Expressions | Automata Theory | Automata Theory | Turing Machines | Turing Machines | Computational Complexity | Computational Complexity | Context-Free Languages | Context-Free Languages | Chomsky Hierarchy | Chomsky Hierarchy | Formal languages | Formal languages | C. Computacion e Inteligencia Artificial | C. Computacion e Inteligencia Artificial | 2014 | 2014 | ía Informática | ía Informática | Regular Languages | Regular Languages | Push-Down Automata | Push-Down Automata

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Copyright 2015, UC3M http://creativecommons.org/licenses/by-nc-sa/4.0/

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This course is organized around algorithmic issues that arise in machine learning. Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this class, we focus on designing algorithms whose performance we can rigorously analyze for fundamental machine learning problems. This course is organized around algorithmic issues that arise in machine learning. Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this class, we focus on designing algorithms whose performance we can rigorously analyze for fundamental machine learning problems.

Subjects

Machine learning | Machine learning | nonnegative matrix factorization | nonnegative matrix factorization | tensor decomposition | tensor decomposition | tensor rank | tensor rank | border rank | border rank | sparse coding | sparse coding | sparse recovery | sparse recovery | learning mixture model | learning mixture model | matrix completion | matrix completion

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

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The main goals of this course are: to introduce the basic concepts of Machine Learning and Big Data Machine Learning; to describe the main areas, techniques, and processes in Machine Learning; to introduce some of the main tools in (Big Data) Machine Learning The main goals of this course are: to introduce the basic concepts of Machine Learning and Big Data Machine Learning; to describe the main areas, techniques, and processes in Machine Learning; to introduce some of the main tools in (Big Data) Machine Learning

Subjects

Nearest neighbours | Nearest neighbours | Models for regression | Models for regression | MLLIB | MLLIB | Gradient Boosting | Gradient Boosting | Bagging | Bagging | Hyper-parameter optimization | Hyper-parameter optimization | Pyspark | Pyspark | Large scale machine learning | Large scale machine learning | Boosting | Boosting | Spark | Spark | 2016 | 2016 | Random Forests | Random Forests | Machine learning | Machine learning | Decision / regression trees and rules | Decision / regression trees and rules | Feature selection | Feature selection | Model evaluation | Model evaluation | Feature transformation | Feature transformation | ML | ML | Basic pipeline | Basic pipeline | Models for classification | Models for classification | MapReduce | MapReduce | C. Computacion e Inteligencia Artificial | C. Computacion e Inteligencia Artificial | Dimensionality reduction | Dimensionality reduction

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Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Subjects

supervised learning | statistical learning | multivariate function | Support Vector Machines | regression | classification | VC theory | computer vision | computer graphics | bioinformatics

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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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

Subjects

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 | Frames

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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Now that the dust appears to have settled, I?d like to revisit Michael Gove and his comments regarding the First World War made at the start of the year. But rather than get into the ins and outs and rights … Continue reading →

Subjects

Consent | Dissent and Revolution | Machine | The Memory of War | 399 (Customs of war & diplomacy) | UA (Armies: Organization - distribution - military situation) | 399 (Customs of war & diplomacy)

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From debates over the extent to which the tank or the aeroplane contributed to the Allied victory, to the introduction of chemical warfare and the prominence of high-explosive artillery shells, the role of technology in the conduct of the First … Continue reading →

Subjects

Machine | administration | efficiency | labour | logistics

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Many things come to mind when discussing World War One ? the stalemate of trench warfare, the horrors of Passchendaele, the poetry of Sassoon and McRae and the massive loss of life on all sides. It’s easy to forget the … Continue reading →

Subjects

Machine | Material Culture | 355 (Military science) | UG (Military engineering. Air forces)

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This unit will enable you to gain the skills and knowledge required and then to apply those skills to the design of a commercial kitchen outlet. You will work within the constraints of costs, efficiency and food hygiene regulations and with an awareness of health and safety standards. Although you do not need prior experience or knowledge of planning or design, some basic knowledge and understanding of food hygiene and health and safety regulations is advisable. Outcomes On completion of this unit, you should be able to: 1. critically examine the factors affecting the planning, design and financing of a production kitchen 2. analyse the effectiveness of a range of kitchen equipment for a food production kitchen 3. evaluate the effectiveness of a production kitchen design.

Subjects

DL40 35 | Planning | design and finance issues | ergonomics | kitchen layout | equipment choice | Supply of Machinery (Safety) Regulations (1992 and Amended 1994) | cooking | ovens | fryers | refrigeration | freezing | hot hold bain-marie | wash-up facilities | waste disposal | T: Construction and Property (Built Environment) | CATERING / FOOD / LEISURE SERVICES / TOURISM | SCQF Level 8

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Except where expressly indicated otherwise on the face of these materials (i) copyright in these materials is owned by the Scottish Qualification Authority (SQA), and (ii) none of these materials may be Used without the express, prior, written consent of the Colleges Open Learning Exchange Group (COLEG) and SQA, except if and to the extent that such Use is permitted under COLEG's conditions of Contribution and Use of Learning Materials through COLEG’s Repository, for the purposes of which these materials are COLEG Materials. Except where expressly indicated otherwise on the face of these materials (i) copyright in these materials is owned by the Scottish Qualification Authority (SQA), and (ii) none of these materials may be Used without the express, prior, written consent of the Colleges Open Learning Exchange Group (COLEG) and SQA, except if and to the extent that such Use is permitted under COLEG's conditions of Contribution and Use of Learning Materials through COLEG’s Repository, for the purposes of which these materials are COLEG Materials. Licensed to colleges in Scotland only Licensed to colleges in Scotland only http://content.resourceshare.ac.uk/xmlui/bitstream/handle/10949/17761/LicenceSQAMaterialsCOLEG.pdf?sequence=1 http://content.resourceshare.ac.uk/xmlui/bitstream/handle/10949/17761/LicenceSQAMaterialsCOLEG.pdf?sequence=1 SQA SQA

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In this unit we will be looking at the techniques applied by engineers and managers to assist with planning and decision making in order to manufacture a product. We will look at how data can be manipulated to show how to calculate the true cost of manufacture or to help in the decision of whether a project is viable or not. The techniques we will cover are not an exhaustive list but are the ones typically used in manufacturing sites across the world. Some knowledge of basic arithmetic operations is assumed but no advanced mathematics is required for this unit.

Subjects

D4GL 04 | Identifying categories of costs | Manufacturing costs | Allocation of overhead costs | Cost unit rate | Machine hour rate | Depreciation costs | Break-even analysis | Profit and break-even points | Safety margin | Economic batch quantities | Financial appraisal techniques | Cash flow statement | Payback period | Tax | SCQF Level 7

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Licensed to colleges in Scotland only Licensed to colleges in Scotland only Except where expressly indicated otherwise on the face of these materials (i) copyright in these materials is owned by the Scottish Qualification Authority (SQA), and (ii) none of these materials may be Used without the express, prior, written consent of the Colleges Open Learning Exchange Group (COLEG) and SQA, except if and to the extent that such Use is permitted under COLEG's conditions of Contribution and Use of Learning Materials through COLEG Repository for the purposes of which these materials are COLEG Materials. Except where expressly indicated otherwise on the face of these materials (i) copyright in these materials is owned by the Scottish Qualification Authority (SQA), and (ii) none of these materials may be Used without the express, prior, written consent of the Colleges Open Learning Exchange Group (COLEG) and SQA, except if and to the extent that such Use is permitted under COLEG's conditions of Contribution and Use of Learning Materials through COLEG Repository for the purposes of which these materials are COLEG Materials. http://content.resourceshare.ac.uk/xmlui/bitstream/handle/10949/17761/LicenceSQAMaterialsCOLEG.pdf?sequence=1 http://content.resourceshare.ac.uk/xmlui/bitstream/handle/10949/17761/LicenceSQAMaterialsCOLEG.pdf?sequence=1 SQA

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We are used to thinking of the First World War as the moment when society was confronted with the new horrors of industrialised warfare. Indeed, the scholarship of the war has focused in recent years on how far the conflict … Continue reading →

Subjects

Machine | language | Tanks | The Western Front | Warfare | World War One

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http://creativecommons.org/licenses/by-nc-sa/2.0/

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6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered. 

Subjects

machine learning | perceptrons | boosting | support vector machines | Markov | hidden Markov models | HMM | Bayesian networks | statistical inference | regression | clustering | bias | variance | regularization | Generalized Linear Models | neural networks | Support Vector Machine | SVM | mixture models | kernel density estimation | gradient descent | quadratic programming | EM algorithm | orward-backward algorithm | junction tree algorithm | Gibbs sampling

License

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

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This course is an introduction to many of the central issues in a branch of philosophy called philosophy of mind. Some of the questions we will discuss include the following. Can computers think? Is the mind an immaterial thing? Or is the mind the brain? Or does the mind stand to the brain as a computer program stands to the hardware? How can creatures like ourselves think thoughts that are "about" things? (For example, we can all think that Aristotle is a philosopher, and in that sense think "about" Aristotle, but what is the explanation of this quite remarkable ability?) Can I know whether your experiences and my experiences when we look at raspberries, fire trucks and stop lights are the same? Can consciousness be given a scientific explanation?

Subjects

artificial intelligence | psychology | philosophy | Turing Machines | consciousness | computer limitations | computation | neurophysiology | Turing test | the analog/digital distinction | Chinese Room argument | causal efficacy of content | inverted spectrum | mental representation | procedural semantics | connectionism

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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