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Reflective Writing Tools Reflective Writing Tools

Description

This resource has been developed to help students understand self help reflection. This resource has been developed to help students understand self help reflection.

Subjects

ukoer | ukoer | Learning disabilities | Learning disabilities | Learning Skills | Learning Skills | Study skills | Study skills

License

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

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4.297 Special Problems in Architecture Studies (MIT) 4.297 Special Problems in Architecture Studies (MIT)

Description

The course investigates e-Learning systems from a business, policy, technical and legal perspective. The issues presented will be tackled by discussion of the design and structure of the various example systems. The connection between information architectures and the physical workplace of the users will also be examined. The course will be comprised of readings, discussions, guest speakers and group design sessions. Laboratory sessions will be focused on implementation tools and opportunities to create one's own working prototypes. Students will learn to describe information architectures using the Unified Modeling Language (used to specify, design and structure web applications) and XML (to designate meaningful content). The course investigates e-Learning systems from a business, policy, technical and legal perspective. The issues presented will be tackled by discussion of the design and structure of the various example systems. The connection between information architectures and the physical workplace of the users will also be examined. The course will be comprised of readings, discussions, guest speakers and group design sessions. Laboratory sessions will be focused on implementation tools and opportunities to create one's own working prototypes. Students will learn to describe information architectures using the Unified Modeling Language (used to specify, design and structure web applications) and XML (to designate meaningful content).

Subjects

XML | XML | e-Learning | e-Learning | e-Learning systems | e-Learning systems | business | business | policy | policy | technical | technical | legal | legal | design | design | connection between information architectures and the physical workplace | connection between information architectures and the physical workplace | Unified Modeling Language | Unified Modeling Language

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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2.997 Decision Making in Large Scale Systems (MIT) 2.997 Decision Making in Large Scale Systems (MIT)

Description

This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks. This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks.

Subjects

algorithm | algorithm | markov decision process | markov decision process | dynamic programming | dynamic programming | stochastic models | stochastic models | policy iteration | policy iteration | Q-Learning | Q-Learning | reinforcement learning | reinforcement learning | Lyapunov function | Lyapunov function | ODE | ODE | TD-Learning | TD-Learning | value function approximation | value function approximation | linear programming | linear programming | policy search | policy search | policy gradient | policy gradient | actor-critic | actor-critic | experts algorithm | experts algorithm | regret minimization and calibration | regret minimization and calibration | games. | games.

License

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

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9.520-A Networks for Learning: Regression and Classification (MIT) 9.520-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 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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Flu pandemic : how prepared are we? Flu pandemic : how prepared are we?

Description

In this podcast Professor Robert Dingwall, Director of the 'Institute of Science and Society' at the University of Nottingham, and a member of the UK government’s Department of Heath committee on the ethical aspects of pandemic influenza, discusses the causes and potential impact of a flu pandemic on the UK. In particular, examining how prepared the UK government is to cope with the medical and social impacts of a flu pandemic, and what steps we can take as individuals to protect ourselves. In the last century, there were three separate flu pandemics, the most serious of which occurred in 1918, which is estimated to have resulted in the deaths of 50 million people worldwide. Professor Dingwall discusses the likelihood of another flu pandemic happening in the future and t In this podcast Professor Robert Dingwall, Director of the 'Institute of Science and Society' at the University of Nottingham, and a member of the UK government’s Department of Heath committee on the ethical aspects of pandemic influenza, discusses the causes and potential impact of a flu pandemic on the UK. In particular, examining how prepared the UK government is to cope with the medical and social impacts of a flu pandemic, and what steps we can take as individuals to protect ourselves. In the last century, there were three separate flu pandemics, the most serious of which occurred in 1918, which is estimated to have resulted in the deaths of 50 million people worldwide. Professor Dingwall discusses the likelihood of another flu pandemic happening in the future and t

Subjects

UNow | UNow | U-now | u now | U-now | u now | open courseware | open courseware | Learning Team | Learning Team | e-Learning | e-Learning | educational | educational | Creative Commons | Creative Commons | resources | resources | eLeK committee | eLeK committee | information Services | information Services | 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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Virtual yeast cell Virtual yeast cell

Description

This rich learning object is used to introduce yeast cytology to students taking Module D24BS3 Brewery Yeast Management as part of the MSc in Brewing Science. The virtual cell permits the students to understand structure and function of yeast organelles. This rich learning object is used to introduce yeast cytology to students taking Module D24BS3 Brewery Yeast Management as part of the MSc in Brewing Science. The virtual cell permits the students to understand structure and function of yeast organelles.

Subjects

UNow | UNow | U-now | U-now | u now | u now | open courseware | open courseware | Learning Team | Learning Team | e-Learning | e-Learning | educational | educational | Creative Commons | Creative Commons | resources | resources | eLeK committee | information Services | eLeK committee | information Services | 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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Rough Guides to Learning and Teaching: Designing, delivering and assessing practical and lab-based sessions

Description

This resource offers practical guidance for teaching and learning support staff involved in designing, facilitating and assessing practical and laboratory-based sessions in HE. It reviews the rationale for using this type of session and the type of learning and skills development it can enable; considers the effective planning and design of experiential learning;how to design effective assessment for learning into this type of activity; and explores the very important issue of safety. PLEASE NOTE: this document carries an ISBN which should not be used on any derivative works.

Subjects

Curriculum Design; PgCERT | Rough Guides to Learning and Teaching | UKOER | JISC OER3 OMAC | Staff Guide | Practical Guidance | Designing | delivering and assessing practical and laboratory based sessions | Practical Learning | Skills Development | Learning environments and safety | Designing Learning | TeessideOMAC3 | PgCert

License

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

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Staff Guide to Distance Learning

Description

This resource encompasses a range of Distance Learning contexts from the traditionally understood correspondence course through to elements of a traditional on-campus programme requiring students to spend periods of time off-campus or at a distance, perhaps on a work-based learning placement. The Introduction to Designing Distance Learning resource available at: http://dspace.jorum.ac.uk/xmlui/handle/123456789/17505 can be used in conjunction with this document.

Subjects

Staff Guide | Practical Guidance | Distance Learning | Designing Distance Learning | UKOER | JISC OER3 OMAC | PgCert | TeessideOMAC3 | Learning at a distance | Curriculum Development | Curriculum Design

License

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

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Introduction to Designing Distance Learning

Description

Learning from engagement with this resource could support demonstration of UKPSF (2011)A1,4,5;CK1-4,6; PV:1-3. It can also be used in conjunction with the Staff Guide to Distance Learning found at: http://dspace.jorum.ac.uk/xmlui/handle/123456789/17504

Subjects

UKOER | JISC OER3 OMAC | Staff Guide | Designing Distance Learning | Experiential Learning for HE staff | UKPSF | UKPSF Mapping | UK Professional Standards in Teaching and Supporting Learning in HE | PgCert | TeessideOMAC3 | Curriculum Design

License

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

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11.124 Introduction to Teaching and Learning Mathematics and Science (MIT) 11.124 Introduction to Teaching and Learning Mathematics and Science (MIT)

Description

This course provides an introduction to teaching and learning in a variety of K-12 settings. Through visits to schools, classroom discussions, selected readings, and hands-on activities, we explore the challenges and opportunities of teaching. Topics of study include educational technology, design and experimentation, student learning, and careers in education. This course provides an introduction to teaching and learning in a variety of K-12 settings. Through visits to schools, classroom discussions, selected readings, and hands-on activities, we explore the challenges and opportunities of teaching. Topics of study include educational technology, design and experimentation, student learning, and careers in education.

Subjects

Teaching | Teaching | Learning | Learning | K-12 | K-12 | Classroom | Classroom | Challenges and Opportunities of teaching | Challenges and Opportunities of teaching | Educational Technology | Educational Technology | Design | Design | Experimentation | Experimentation | Teaching methods | Teaching methods | Teaching techniques | Teaching techniques

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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11.124 Introduction to Teaching and Learning Mathematics and Science (MIT) 11.124 Introduction to Teaching and Learning Mathematics and Science (MIT)

Description

This course provides an introduction to teaching and learning in a variety of K-12 settings. Through visits to schools, classroom discussions, selected readings, and hands-on activities, we explore the challenges and opportunities of teaching. Topics of study include educational technology, design and experimentation, student learning, and careers in education.Technical RequirementsStarLogo software is required to run the .slogo files found on this course site. This course provides an introduction to teaching and learning in a variety of K-12 settings. Through visits to schools, classroom discussions, selected readings, and hands-on activities, we explore the challenges and opportunities of teaching. Topics of study include educational technology, design and experimentation, student learning, and careers in education.Technical RequirementsStarLogo software is required to run the .slogo files found on this course site.

Subjects

Teaching | Teaching | Learning | Learning | K-12 | K-12 | Classroom | Classroom | Challenges and Opportunities of teaching | Challenges and Opportunities of teaching | Educational Technology | Educational Technology | Design | Design | Experimentation | Experimentation | Teaching methods | Teaching methods | Teaching techniques | Teaching techniques

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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8.01L Physics I: Classical Mechanics (MIT) 8.01L Physics I: Classical Mechanics (MIT)

Description

8.01L is an introductory mechanics course, which covers all the topics covered in 8.01T. The class meets throughout the fall, and continues throughout the Independent Activities Period (IAP). 8.01L is an introductory mechanics course, which covers all the topics covered in 8.01T. The class meets throughout the fall, and continues throughout the Independent Activities Period (IAP).

Subjects

Introductory classical mechanics | Introductory classical mechanics | space | space | time | time | straight-line kinematics | straight-line kinematics | motion in a plane | motion in a plane | forces | forces | static equilibrium | static equilibrium | particle dynamics | particle dynamics | conservation of momentum | conservation of momentum | relative inertial frames | relative inertial frames | non-inertial force | non-inertial force | work | work | potential energy | potential energy | conservation of energy | conservation of energy | ideal gas | ideal gas | rigid bodies | rigid bodies | rotational dynamics | rotational dynamics | vibrational motion | vibrational motion | conservation of angular momentum | conservation of angular momentum | central force motions | central force motions | fluid mechanics | fluid mechanics | Technology-Enabled Active Learning | Technology-Enabled Active Learning

License

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

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9.95-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 Itself

Subjects

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 | organization

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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2.997 Decision Making in Large Scale Systems (MIT)

Description

This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks.

Subjects

algorithm | markov decision process | dynamic programming | stochastic models | policy iteration | Q-Learning | reinforcement learning | Lyapunov function | ODE | TD-Learning | value function approximation | linear programming | policy search | policy gradient | actor-critic | experts algorithm | regret minimization and calibration | games.

License

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

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9.520-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 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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KMR49131 : HIV and AIDS Prevention Education

Description

Course Information Background HIV and AIDS have been declared as a worldwide threatening for the nation development. AIDS reduces the life expectancy and economic potential, increasing the vulnerability of future generation by creating millions of orphans, and diminishing the capacity of public and private sectors (UNAIDS). Nowadays, over 20 million people are now living with […]

Subjects

Faculty of Public Health | 021-787 4265 | Acronyms | Africa | AIDS | AIDS Prevention | Asia | Asia Region HIV/AIDS Epidemic | Benjamin Lara | Credits For University | Dili | Dili Distance Learning Center World Bank Timor-Leste Office | disease | diseases | Distance Learning | epidemic | Ferchito L. Avelino | Governor | Health | HIV | HIV Prevention | HIV/AIDS | HIV/AIDS in China | Indonesia | Jakarta | Kamal Hisham Kamaruddin | Karina Razali Timor Leste | Laguna | Malaysia | Medicine | National Holiday in Indonesia | National Minority AIDS Council | Pandemics | Philippines | Prevention of HIV | Procedure University | Rui Calvarho | sub-Saharan Africa | Syndromes | UNAIDS | United Nations | United Nations Educational Scientific and Cultural Organization | University of Indonesia | Venue For University of Indonesia’s Student | Vicente Soares

License

http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en_US

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Intellectual Capital and Knowledge Management Intellectual Capital and Knowledge Management

Description

How can organizations manage the accumulation and flow of knowledge to sustain competitive advantage? In the new enterprise management the employees are in the centre. In teamwork new ideas are shared and developed, and this is organizational learning. Another question is: How organization can measure it? This is intellectual capital. In summary, the knowledge of the employees is the most important resource, because investment only in technical equipments is not enough to have an advantage in know-how. Working in teams at the right place, employees explore and exploit knowledge. How can organizations manage the accumulation and flow of knowledge to sustain competitive advantage? In the new enterprise management the employees are in the centre. In teamwork new ideas are shared and developed, and this is organizational learning. Another question is: How organization can measure it? This is intellectual capital. In summary, the knowledge of the employees is the most important resource, because investment only in technical equipments is not enough to have an advantage in know-how. Working in teams at the right place, employees explore and exploit knowledge.

Subjects

Organizational Learning | Organizational Learning | Knowledge Management | Knowledge Management | Organización de empresas | Organización de empresas | Intellectual Capital | Intellectual Capital | Innovation | Innovation | Clusters | Clusters

License

Copyright 2009, by the Contributing Authors http://creativecommons.org/licenses/by-nc-sa/3.0/

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Introducción a los Sistemas de Tiempo Real Introducción a los Sistemas de Tiempo Real

Description

La materia contenida en la asignatura está directamente relacionada con el desarrollo de software en entornos industriales y sistemas de alta integridad. En general se aplicará a sistemas en los que, debido a su interacción con entornos físicos, tienen gran importancia el cumplimiento de tiempos de respuesta deterministas y la seguridad e integridad del sistema. Son muchos los ámbitos de aplicación de esta materia, pudiendo citar entre ellos: automoción, robótica, ingeniería espacial, aviónica, domótica o sistemas multimedia. Esta asignatura pretende cubrir prácticamente todas las fases del ciclo de vida de un sistema de tiempo real, por este motivo está directamente relacionada con varias asignaturas de su contexto. El detalle de estas relaciones se aborda en la siguiente sec La materia contenida en la asignatura está directamente relacionada con el desarrollo de software en entornos industriales y sistemas de alta integridad. En general se aplicará a sistemas en los que, debido a su interacción con entornos físicos, tienen gran importancia el cumplimiento de tiempos de respuesta deterministas y la seguridad e integridad del sistema. Son muchos los ámbitos de aplicación de esta materia, pudiendo citar entre ellos: automoción, robótica, ingeniería espacial, aviónica, domótica o sistemas multimedia. Esta asignatura pretende cubrir prácticamente todas las fases del ciclo de vida de un sistema de tiempo real, por este motivo está directamente relacionada con varias asignaturas de su contexto. El detalle de estas relaciones se aborda en la siguiente sec

Subjects

Multitarea | Multitarea | Plazo de respuesta | Plazo de respuesta | Project Based Learning | Project Based Learning | Arquitectura y Tecnología de Computadores | Arquitectura y Tecnología de Computadores | Restricciones temporales | Restricciones temporales | Sistemas alta integridad | Sistemas alta integridad | Planificación por prioridades | Planificación por prioridades | Lenguaje Ada | Lenguaje Ada | Programas concurrentes | Programas concurrentes | Competencias transversales | Competencias transversales | RMA | RMA | Multiprogramación | Multiprogramación | Sistemas críticos | Sistemas críticos | Planificación expulsora | Planificación expulsora | HRT-HOOD | HRT-HOOD | Tiempo de respuesta | Tiempo de respuesta | Planificación | Planificación | Procesos periódicos | Procesos periódicos | Aprendizaje basado en proyecto | Aprendizaje basado en proyecto | Fiabilidad | Fiabilidad | Monótono en frecuencia | Monótono en frecuencia | PBL | PBL | Planificación de procesos | Planificación de procesos | Sistemas de tiempo real | Sistemas de tiempo real | ABP | ABP | Procesos aperiódicos | Procesos aperiódicos | Statecharts | Statecharts | Sincronización de procesos | Sincronización de procesos | Bloques de recuperación | Bloques de recuperación | Tolerancia a fallos | Tolerancia a fallos | Programación concurrente | Programación concurrente | Competencias profesionales | Competencias profesionales | Prioridades de procesos | Prioridades de procesos | Ada95 | Ada95

License

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n Automtica n Automtica

Description

Esta asignatura aborda el tema de la Programacin Automtica desde una perspectiva de Aprendizaje Automtico. La asignatura comienza tratando los primeros intentos dentro de la Inteligencia Artificial de aprender programas en LISP mediante la tcnica de Summers. Pero el objetivo de la asignatura es mas amplio y se centra en el aprendizaje inductivo de estructuras que tengan la potencia de un programa de ordenador o de atacar tareas que para su resolucin requeriran escribir un programa. Esta asignatura aborda el tema de la Programacin Automtica desde una perspectiva de Aprendizaje Automtico. La asignatura comienza tratando los primeros intentos dentro de la Inteligencia Artificial de aprender programas en LISP mediante la tcnica de Summers. Pero el objetivo de la asignatura es mas amplio y se centra en el aprendizaje inductivo de estructuras que tengan la potencia de un programa de ordenador o de atacar tareas que para su resolucin requeriran escribir un programa.

Subjects

todo de Summers | todo de Summers | n de distribuciones | n de distribuciones | ster | ster | n Gentica | n Gentica | PIPE | PIPE | Automatic Design of Algorithms | Automatic Design of Algorithms | Learning Classifier Systems | Learning Classifier Systems | Aprendizaje por refuerzo avanzado | Aprendizaje por refuerzo avanzado | ILP | ILP | C. Computacion e Inteligencia Artificial | C. Computacion e Inteligencia Artificial | Programas funcionales | Programas funcionales | n lgica inductiva | n lgica inductiva | n evolutiva | n evolutiva | 2013 | 2013

License

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

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i, fy hawliau i i, fy hawliau i

Description

Subjects

ukoer | ukoer | Learning disabilities | Learning disabilities

License

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

Site sourced from

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Gall rhywbeth bach wneud gwahaniaeth mawr Gall rhywbeth bach wneud gwahaniaeth mawr

Description

Subjects

ukoer | ukoer | Learning disabilities | Learning disabilities

License

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

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Dyma Steve Dyma Steve

Description

Subjects

ukoer | ukoer | Learning disabilities | Learning disabilities

License

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

Site sourced from

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Ymateb i fynegiant o rywioldeb1 Ymateb i fynegiant o rywioldeb1

Description

Subjects

ukoer | ukoer | Learning disabilities | Learning disabilities

License

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

Site sourced from

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Baby first! Baby first!

Description

A resource designed to help healthcare professionals to support families over the first important year of life when their baby has an intellectual disability. A resource designed to help healthcare professionals to support families over the first important year of life when their baby has an intellectual disability.

Subjects

ukoer | ukoer | Learning disabilities | Learning disabilities | Midwifery | Midwifery

License

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

Site sourced from

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Little Things Make a Big Difference Little Things Make a Big Difference

Description

This RLO is based around an interaction between a healthcare professional and a client with learning disabilities. It shows how important even small aspects of communication are in formulating a positive interaction. This RLO is based around an interaction between a healthcare professional and a client with learning disabilities. It shows how important even small aspects of communication are in formulating a positive interaction.

Subjects

ukoer | ukoer | Learning disabilities | Learning disabilities

License

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

Site sourced from

http://sonet.nottingham.ac.uk/rlos/all_rlo_rssfeed.php

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