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11.233 Research Design for Policy Analysis and Planning (MIT) 11.233 Research Design for Policy Analysis and Planning (MIT)

Description

This course develops skills in research design for policy analysis and planning. The emphasis is on the logic of the research process and its constituent elements. The course relies on a seminar format so students are expected to read all of the assigned materials and come to class prepared to discuss key themes, ideas, and controversies. Since the materials draw broadly on the social sciences, and since students have diverse interests and methodological preferences, ongoing themes in our discussions will be linking concepts to planning scholarship in general and considering how different epistemological orientations and methodological techniques map on to planning specializations. This course develops skills in research design for policy analysis and planning. The emphasis is on the logic of the research process and its constituent elements. The course relies on a seminar format so students are expected to read all of the assigned materials and come to class prepared to discuss key themes, ideas, and controversies. Since the materials draw broadly on the social sciences, and since students have diverse interests and methodological preferences, ongoing themes in our discussions will be linking concepts to planning scholarship in general and considering how different epistemological orientations and methodological techniques map on to planning specializations.

Subjects

policy and planning research | policy and planning research | theories | theories | research questions | research questions | research proposals | research proposals | research design | research design | experimental designs | experimental designs | research ethics | research ethics | sampling | sampling | surveys | surveys | questionnaires | questionnaires | interviewing | interviewing | case studies | case studies | field research | field research | participatory research | participatory research | action research | action research | unobtrusive measures | unobtrusive measures

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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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

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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16.410 Principles of Autonomy and Decision Making (MIT) 16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search trees

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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DATUM for Health: Research data management training for health studies

Description

The DATUM for Health training programme is aimed at postgraduate research (i.e. doctoral) students (PGR) in health studies, including those whose PhD has a health focus but who are not necessarily registered in a school/faculty of health/medicine (e.g. in psychology, social sciences). The programme covers both generic and discipline-specific issues, focussing on the management of qualitative, unstructured data, and is suitable for students at any stage of their PhD. It aims to provide PGR students with the knowledge to manage their research data at every stage in the data lifecycle, from its creation to its final storage or destruction. Students learn how to use their data more effectively and efficiently, how to store and destroy it securely, and how to make it available to a wider audien

Subjects

research | researchers | research data | research data management | research data management training | data management | data management training | organising research data | organising data | study skills | research skills | health | medicine | doctoral students | postgraduate research students | qualitative data | unstructured data | data curation | data curation lifecycle | pgr students | phd students | datum | datum for health | data | health data | curation research data | data organisation | organisation research data | rdm | jisc research data management training materials | jisc rdmtrain | RDMTrain

License

Attribution-NonCommercial-ShareAlike 3.0 Unported Attribution-NonCommercial-ShareAlike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ http://creativecommons.org/licenses/by-nc-sa/3.0/

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17.869 Political Science Scope and Methods (MIT) 17.869 Political Science Scope and Methods (MIT)

Description

This course is designed to provide an introduction to a variety of empirical research methods used by political scientists. The primary aims of the course are to make you a more sophisticated consumer of diverse empirical research and to allow you to conduct sophisticated independent work in your junior and senior years. This is not a course in data analysis. Rather, it is a course on how to approach political science research. This course is designed to provide an introduction to a variety of empirical research methods used by political scientists. The primary aims of the course are to make you a more sophisticated consumer of diverse empirical research and to allow you to conduct sophisticated independent work in your junior and senior years. This is not a course in data analysis. Rather, it is a course on how to approach political science research.

Subjects

political science | political science | empirical research | empirical research | scientific method | scientific method | research design | research design | models | models | samping | samping | statistical analysis | statistical analysis | measurement | measurement | ethics | ethics | empirical | empirical | research | research | scientific | scientific | methods | methods | statistics | statistics | statistical | statistical | analysis | analysis | political | political | politics | politics | science | science | design | design | sampling | sampling | theoretical | theoretical | observation | observation | data | data | case studies | case studies | cases | cases | empirical research methods | empirical research methods | political scientists | political scientists | empirical analysis | empirical analysis | theoretical analysis | theoretical analysis | research projects | research projects | department faculty | department faculty | inference | inference | writing | writing | revision | revision | oral presentations | oral presentations | experimental method | experimental method | theories | theories | political implications | political implications

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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15.347 Doctoral Seminar in Research Methods I (MIT) 15.347 Doctoral Seminar in Research Methods I (MIT)

Description

This course is designed to lay the foundations of good empirical research in the social sciences. It does not deal with specific techniques per se, but rather with the assumptions and the logic underlying social research. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects and to evaluate the products of empirical research. This course is designed to lay the foundations of good empirical research in the social sciences. It does not deal with specific techniques per se, but rather with the assumptions and the logic underlying social research. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects and to evaluate the products of empirical research.

Subjects

good empirical research | good empirical research | social sciences | social sciences | assumptions and the logic underlying social research | assumptions and the logic underlying social research | research design | research design | research projects | research projects | products of empirical research | products of empirical research

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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15.347 Doctoral Seminar in Research Methods I (MIT) 15.347 Doctoral Seminar in Research Methods I (MIT)

Description

This course is designed to lay the foundations of good empirical research in the social sciences. It does not deal with specific techniques per se, but rather with the assumptions and the logic underlying social research. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects and to evaluate the products of empirical research. This course is designed to lay the foundations of good empirical research in the social sciences. It does not deal with specific techniques per se, but rather with the assumptions and the logic underlying social research. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects and to evaluate the products of empirical research.

Subjects

good empirical research | good empirical research | social sciences | social sciences | assumptions and the logic underlying social research | assumptions and the logic underlying social research | research design | research design | research projects | research projects | products of empirical research | products of empirical research

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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17.878 Qualitative Research: Design and Methods (MIT) 17.878 Qualitative Research: Design and Methods (MIT)

Description

This seminar explores the development and application of qualitative research designs and methods in political analysis. It considers a broad array of approaches, from exploratory narratives to focused-comparison case studies, for investigating plausible alternative hypotheses. The focus is on analysis, not data collection. This seminar explores the development and application of qualitative research designs and methods in political analysis. It considers a broad array of approaches, from exploratory narratives to focused-comparison case studies, for investigating plausible alternative hypotheses. The focus is on analysis, not data collection.

Subjects

development and application of qualitative research designs and methods in political analysis | development and application of qualitative research designs and methods in political analysis | exploratory narrative | exploratory narrative | focused-comparison case studies | focused-comparison case studies | investigating plausible alternative hypotheses | investigating plausible alternative hypotheses | research methods | research methods | methodology | methodology | rival hypothesis | rival hypothesis | research designs | research designs | plausibility | plausibility | political analysis | political analysis | data analysis | data analysis | validity | validity | reliability | reliability | inference | inference | observations | observations | cases | cases | subjects | subjects | research agenda | research agenda | qualitative methods | qualitative methods | qualitative research | qualitative research

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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Research Information Management: Organising Humanities Material

Description

Research Information Management: Organising Humanities Material is a course for humanities researchers (including graduate students), designed to occupy about half a day. Included are a course book, a set of sample files for use during course exercises, and a slideshow for classroom use (the course book and exercise files can also be used for individual study). Topics covered include: identifying your working style; organising paper and electronic material; file and folder structures; tagging vs. hierarchical filing; retrieving information; and linking notes and sources. These course materials are part of a set of resources created as part of the Sudamih Project at Oxford University Computing Services.

Subjects

research | researchers | research data | research data management | research information | research information management | data management | personal information managment | organising research information | organising research material | organisation | folder structures | file structures | hierarchical filing | tag-based filing | information retrieval | linking information | software tools | study skills | research skills | Education | X000

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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Research Information Management: Tools for the Humanities

Description

Research Information Management: Tools for the Humanities is a course for humanities researchers, designed to occupy about half a day. Included are a course book, a set of sample files for use during course exercises, and a slideshow for classroom use (the course book and exercise files can also be used for individual study). Topics covered include: selecting appropriate software tools; organising electronic material; retrieving information; integrating varied material; and when to consider using a database. The course introduces a range of software tools, many of which are available free of charge. These course materials are part of a set of resources created as part of the Sudamih Project at Oxford University Computing Services.

Subjects

research | researchers | research data | research data management | research information | research information management | data management | personal information managment | organising research information | organising research material | organisation | information retrieval | software tools | bibliographic software | annotation software | study skills | research skills | Education | X000

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 Research Data Management - slideshows

Description

These slideshows provide a brief introduction to key issues in research data management. They are designed for use in induction events, or as part of a research skills course. Three versions are provided: all cover broadly the same ground, but in different degrees of detail. The longest of the three includes some issues that are of particular relevance to researchers at postdoctoral level or above. These slideshows are part of a set of resources created as part of the Sudamih Project at Oxford University Computing Services. (NB. The slides use an OUCS design template, but users are welcome to transfer the content to their own institutional template as appropriate.)

Subjects

research | researchers | postgraduate research students | postdoctoral researchers | research data | research data management | research information | research information management | backing up | information retrieval | study skills | research skills | Education | X000

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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16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

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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6.006 Introduction to Algorithms (MIT) 6.006 Introduction to Algorithms (MIT)

Description

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems. This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

Subjects

algorithms | algorithms | python | python | python cost model | python cost model | binary search trees | binary search trees | hashing | hashing | sorting | sorting | searching | searching | shortest paths | shortest paths | dynamic programming | dynamic programming | numerics | numerics | document distance | document distance | longest common substring | longest common substring | dijkstra | dijkstra | fibonacci | fibonacci | image resizing | image resizing | chaining | chaining | hash functions | hash functions | priority queues | priority queues | breadth first search | breadth first search | depth first search | depth first search | memoization | memoization | divide and conquer | divide and conquer

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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NACA Research Pilot Howard Clifton Lilly NACA Research Pilot Howard Clifton Lilly

Description

Subjects

nasa | nasa | naca | naca | speedofsound | speedofsound | testpilot | testpilot | nasalangleyresearchcenter | nasalangleyresearchcenter | nasadrydenflightresearchcenter | nasadrydenflightresearchcenter | nasaglennresearchcenter | nasaglennresearchcenter | armstrongflightresearchcenter | armstrongflightresearchcenter | howardlilly | howardlilly

License

No known copyright restrictions

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17.878 Qualitative Research: Design and Methods (MIT) 17.878 Qualitative Research: Design and Methods (MIT)

Description

This course is intended for graduate students planning to conduct qualitative research in a variety of different settings. Its topics include: Case studies, interviews, documentary evidence, participant observation, and survey research. The primary goal of this course is to assist students in preparing their (Masters and PhD) dissertation proposals. This course is intended for graduate students planning to conduct qualitative research in a variety of different settings. Its topics include: Case studies, interviews, documentary evidence, participant observation, and survey research. The primary goal of this course is to assist students in preparing their (Masters and PhD) dissertation proposals.

Subjects

qualitative research | qualitative research | survey research | survey research | interviewing | interviewing | participant observation | participant observation | case studies | case studies | social science | social science | social science research | social science research | research design | research design | documentary evidence | documentary evidence | fieldwork | fieldwork

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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11.233 Research Design for Policy Analysis and Planning (MIT)

Description

This course develops skills in research design for policy analysis and planning. The emphasis is on the logic of the research process and its constituent elements. The course relies on a seminar format so students are expected to read all of the assigned materials and come to class prepared to discuss key themes, ideas, and controversies. Since the materials draw broadly on the social sciences, and since students have diverse interests and methodological preferences, ongoing themes in our discussions will be linking concepts to planning scholarship in general and considering how different epistemological orientations and methodological techniques map on to planning specializations.

Subjects

policy and planning research | theories | research questions | research proposals | research design | experimental designs | research ethics | sampling | surveys | questionnaires | interviewing | case studies | field research | participatory research | action research | unobtrusive measures

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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Readme file for Introduction to OO Programming in Java

Description

This readme file contains details of links to all the Introduction to OO Programming in Java module's material held on Jorum and information about the module as well.

Subjects

ukoer | programming task guide | programming lecture | programming reading material | software design reading material | classes guide | libraries lecture | classes reading material | classes visual aid | software objects guide | graphics reading material | attributes reading material | attributes visual guide | naming conventions reading material | code reading material | java keywords reading material | variables visual guide | arithmetic reading material | java assignment | making decisions task guide | making decisions lecture | making decisions reading material | boolean expressions visual guide | repetition reading material | while loops visual guide | methods reading material | methods practical | access modifiers reading material | objects reading material | classes assignment | classes practical | child classes task guide | inheritance task guide | extending classes lecture | inheritance reading material | inheritance visual guide | inheritance practical | graphics task guide | awt reading material | graphics visual aid | awt class library reading material | event-driven programming reading material | scrollbars reading material | reflective practice visual guide | mobile phone task guide | mobile phone lecture | fixed repitition reading material | fixed repitition visual guide | mobile phone library reading material | mobile phone reading material | arrays task guide | arrays lecture | arrays reading material | arrays visual guide | creating software objects reading material | software objects visual guide | java practical | generic array list task guide | overriding methods reading material | menu and switch task guide | multi-way decisions reading material | multi-way decisions visual guide | searching task guide | searching lecture | searching reading material | software quality task guide | software quality lecture | software quality reading material | programming assignment | applet reading material | classes visual guide | object-oriented programming | object-oriented | programming | java | problem solving | java program | software design | programming languages | computers | class task guide | class reading material | class assignment | class practical | java classes | variables | attributes | arithmetic | java class | classes and arithmetic | classes | class | decisions | boolean expression | boolean expressions | repetition | methods | aggregate classes | access modifier | access modifiers | child classes | inheritance | child class | graphics | awt class library | fixed repetition | for loop | for loops | array | arrays | iteration | software object | definite iteration | generic lists | generic array list | cast | casting | overriding method | overriding methods | generic list | menu-driven program | menu-driven programs | multi-way decisions | menu and switch | search | searching | software quality | testing | software quality and testing | assessment | computers task guide | programming languages task guide | software design task guide | java program task guide | problem-solving task guide | problem solving task guide | object-oriented programming task guide | java task guide | object-oriented task guide | object oriented task guide | computers lecture | programming languages lecture | software design lecture | java program lecture | problem solving lecture | object-oriented programming lecture | java lecture | object oriented programming lecture | object-oriented lecture | computers reading material | programming languages reading material | java program reading material | problem solving reading material | object-oriented programming reading material | java reading material | object-oriented reading material | object oriented reading material | java classes task guide | variables task guide | attributes task guide | arithmetic task guide | java class task guide | classes and arithmetic task guide | classes task guide | java classes lecture | variables lecture | attributes lecture | arithmetic lecture | java class lecture | classes and arithmetic lecture | classes lecture | class lecture | java classes reading material | variables reading material | java class reading material | classes and arithmetic reading material | java classes visual aid | variables visual aid | attributes visual aid | arithmetic visual aid | java class visual aid | classes and arithmetic visual aid | class visual aid | java visual aid | object-oriented programming visual aid | programming visual aid | object-oriented visual aid | decisions task guide | boolean expression task guide | boolean expressions task guide | repetition task guide | methods task guide | decisions lecture | boolean expression lecture | boolean expressions lecture | repetition lecture | methods lecture | decisions reading material | boolean expression reading material | boolean expressions reading material | decisions visual aid | boolean expression visual aid | boolean expressions visual aid | repetition visual aid | methods visual aid | decisions practical | boolean expression practical | boolean expressions practical | repetition practical | programming practical | object oriented programming practical | object-oriented programming practical | object-oriented practical | object oriented practical | aggregate classes task guide | access modifier task guide | access modifiers task guide | aggregate classes lecture | access modifier lecture | access modifiers lecture | aggregate classes reading material | access modifier reading material | aggregate classes assignment | java classes assignment | access modifier assignment | access modifiers assignment | object oriented programming assignment | object-oriented programming assignment | object-oriented assignment | object oriented assignment | child class task guide | child classes lecture | inheritance lecture | child class lecture | child classes reading material | child class reading material | child classes visual aid | inheritance visual aid | child class visual aid | awt class library task guide | graphics lecture | awt class library lecture | awt class library visual aid | graphics assignment | awt class library assignment | fixed repetition task guide | fixed repetition lecture | fixed repetition visual aid | fixed repetition reading material | for loop task guide | for loops task guide | array task guide | iteration task guide | software object task guide | definite iteration task guide | for loop lecture | for loops lecture | array lecture | iteration lecture | software object lecture | definite iteration lecture | for loop reading material | for loops reading material | array reading material | iteration reading material | software object reading material | definite iteration reading material | for loop visual aid | for loops visual aid | array visual aid | arrays visual aid | iteration visual aid | software object visual aid | definite iteration visual aid | generic lists task guide | cast task guide | casting task guide | overriding method task guide | overriding methods task guide | generic list task guide | generic lists lecture | generic array list lecture | cast lecture | casting lecture | overriding method lecture | overriding methods lecture | generic list lecture | generic lists reading material | generic array list reading material | cast reading material | casting reading material | overriding method reading material | generic list reading material | menu-driven program task guide | menu-driven programs task guide | multi-way decisions task guide | menu-driven program lecture | menu-driven programs lecture | multi-way decisions lecture | menu and switch lecture | menu-driven program reading material | menu-driven programs reading material | menu and switch reading material | menu-driven program visual aid | menu-driven programs visual aid | multi-way decisions visual aid | menu and switch visual aid | search task guide | search lecture | search reading material | testing task guide | software quality and testing task guide | testing lecture | software quality and testing lecture | testing reading material | software quality and testing reading material | assessment reading material | assessment assignment | fixed repetition practical | jcreator guide | g622 | oo | oop | oo programming | awt | oo programming task guide | oop task guide | oo task guide | g622 task guide | oo programming lecture | oop lecture | oo lecture | g622 lecture | oo programming reading material | oop reading material | oo reading material | g622 reading material | g622 visual aid | oop visual aid | oo visual aid | oo programming visual aid | g622 practical | oo practical | oo programming practical | oop practical | g622 assignment | oo assignment | oop assignment | oo programming assignment | awt task guide | awt lecture | awt visual aid | awt assignment | Computer science | I100

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Research Information Management Guides

Description

The Research Information Management Guides are a series of articles covering a range of topics relating to the management of material used in the course of a research project. They include tips for good practice, and details of useful software tools. Areas covered include: organising research material; bibliographic software; note taking; working with structured data; and file and document management. The Guides are part of a set of resources created as part of the Sudamih Project at Oxford University Computing Services.

Subjects

research | research data | research data management | research information | research information management | personal information managment | organising research information | organising research material | organisation | bibliographic software | note taking | structured data | data management planning | databases | relational databases | structuring data | documenting data | data sharing | data curation | data archiving | data ethics | file naming | file synchronisation | versioning | study skills | research skills | Education | X000

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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16.410 Principles of Autonomy and Decision Making (MIT)

Description

This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information

Subjects

autonomy | decision | decision-making | reasoning | optimization | autonomous | autonomous systems | decision support | algorithms | artificial intelligence | a.i. | operations | operations research | logic | deduction | heuristic search | constraint-based search | model-based reasoning | planning | execution | uncertainty | machine learning | linear programming | dynamic programming | integer programming | network optimization | decision analysis | decision theoretic planning | Markov decision process | scheme | propositional logic | constraints | Markov processes | computational performance | satisfaction | learning algorithms | system state | state | search treees | plan spaces | model theory | decision trees | function approximators | optimization algorithms | limitations | tradeoffs | search and reasoning | game tree search | local stochastic search | stochastic | genetic algorithms | constraint satisfaction | propositional inference | rule-based systems | rule-based | model-based diagnosis | neural nets | reinforcement learning | web-based | search trees

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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Research information management guides

Description

The Research Information Management Guides are a series of articles covering a range of topics relating to the management of material used in the course of a research project. They include tips for good practice, and details of useful software tools. Areas covered include: organising research material; bibliographic software; note taking; working with structured data; and file and document management. The Guides are part of a set of resources created by the Sudamih Project at Oxford University Computing Services in 2011.

Subjects

research | research data | research data management | research information management | personal information managment | organising research information | organising research material | organisation | bibliographic software | note taking | structured data | data management planning | databases | relational databases | structuring data | documenting data | data sharing | data curation | data archiving | data ethics | file naming | file synchronisation | versioning | study skills | research skills

License

Attribution-NonCommercial-ShareAlike 3.0 Unported Attribution-NonCommercial-ShareAlike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ http://creativecommons.org/licenses/by-nc-sa/3.0/

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Research information management: Organising humanities material

Description

Research Information Management: Organising Humanities Material is a course for humanities researchers (including graduate students), designed to occupy about half a day. Included are a course book, a set of sample files for use during course exercises, and a slideshow for classroom use (the course book and exercise files can also be used for individual study). Topics covered include: identifying your working style; organising paper and electronic material; file and folder structures; tagging vs. hierarchical filing; retrieving information; and linking notes and sources. These course materials are part of a set of resources created by the Sudamih Project at Oxford University Computing Services in 2011.

Subjects

research | researchers | research data | research data management | personal information managment | organising research information | organising research material | organisation | folder structures | file structures | hierarchical filing | tag-based filing | information retrieval | linking information | software tools | study skills | research skills

License

Attribution-NonCommercial-ShareAlike 3.0 Unported Attribution-NonCommercial-ShareAlike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ http://creativecommons.org/licenses/by-nc-sa/3.0/

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Research information management: Tools for the humanities

Description

Research Information Management: Tools for the Humanities is a course for humanities researchers, designed to occupy about half a day. Included are a course book, a set of sample files for use during course exercises, and a slideshow for classroom use (the course book and exercise files can also be used for individual study). Topics covered include: selecting appropriate software tools; organising electronic material; retrieving information; integrating varied material; and when to consider using a database. The course introduces a range of software tools, many of which are available free of charge. These course materials are part of a set of resources created by the Sudamih Project at Oxford University Computing Services in 2011.

Subjects

research | researchers | research data | research data management | personal information managment | organising research information | organising research material | organisation | information retrieval | software tools | bibliographic software | annotation software | study skills | research skills

License

Attribution-NonCommercial-ShareAlike 3.0 Unported Attribution-NonCommercial-ShareAlike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ http://creativecommons.org/licenses/by-nc-sa/3.0/

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6.851 Advanced Data Structures (MIT) 6.851 Advanced Data Structures (MIT)

Description

Includes audio/video content: AV lectures. Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structure. Acknowledgments Thanks to videographers Martin Demaine and Justin Zhang. Includes audio/video content: AV lectures. Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structure. Acknowledgments Thanks to videographers Martin Demaine and Justin Zhang.

Subjects

data | data | structures | structures | data structures | data structures | computers | computers | computer science | computer science | strings | strings | dynamic graphs | dynamic graphs | integers | integers | hash | hash | hashing | hashing | hashish | hashish | hashtag | hashtag | hash tag | hash tag | hash tagger | hash tagger | memory | memory | memory heirarchy | memory heirarchy | binary tree | binary tree | binary search | binary search | binary search tree | binary search tree | time travel | time travel | back to the future | back to the future | forward to the past | forward to the past | database | database | table | table | database table | database table | cache | cache | caching | caching | mad cache money | mad cache money | logarithmic time | logarithmic time | eurythmic time | eurythmic time | operations | operations | search | search | heaps | heaps

License

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

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7.18 Topics in Experimental Biology (MIT) 7.18 Topics in Experimental Biology (MIT)

Description

This independent experimental study course is designed to allow students with a strong interest in independent research to fulfill the project laboratory requirement for the Biology Department Program in the context of a research laboratory at MIT. The research should be a continuation of a previous project under the direction of a member of the Biology Department faculty. This course provides instruction and practice in written and oral communication. Journal club discussions are used to help students evaluate and write scientific papers. This independent experimental study course is designed to allow students with a strong interest in independent research to fulfill the project laboratory requirement for the Biology Department Program in the context of a research laboratory at MIT. The research should be a continuation of a previous project under the direction of a member of the Biology Department faculty. This course provides instruction and practice in written and oral communication. Journal club discussions are used to help students evaluate and write scientific papers.

Subjects

experimental biology | experimental biology | journal club | journal club | primary literature | primary literature | scientific research | scientific research | oral presentations | oral presentations | communication | communication | abstracts | abstracts | materials and methods | materials and methods | discussion | discussion | IMRAD | IMRAD | research report | research report | laboratory research | laboratory research | results section | results section

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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Readme file for Introduction to Artificial Intelligence

Description

This readme file contains details of links to all the Introduction to Artificial Intelligence module's material held on Jorum and information about the module as well.

Subjects

ukoer | evolutionary algorithm lecture | algorithm tutorial | genetic algorithm lecture | genetic algorithm example | evolutionary computation tutorial | artificial intelligence lecture | artificial intelligence tutorial | random processes reading material | semantic web reading material | neural networks video | evolutionary computation test | artificial intelligence test | knowledge representation test | neural networks test | evolutionary algorithm | genetic computation | genetic programming | evolutionary computation | artificial intelligence | introduction to artificial intelligence | search | problem solving | revision | knowledge representation | semantic web | neural network | neural networks | artificial neural networks | swarm intelligence | collective intelligence | robot societies | genetic computation lecture | genetic programming lecture | evolutionary computation lecture | introduction to artificial intelligence lecture | evolutionary algorithm tutorial | genetic computation tutorial | genetic programming tutorial | introduction to artificial intelligence tutorial | evolutionary algorithm example | genetic computation example | genetic programming example | evolutionary computation example | artificial intelligence example | introduction to artificial intelligence example | search lecture | problem solving lecture | search tutorial | problem solving tutorial | search example | problem solving example | revision reading material | search reading material | artificial intelligence reading material | introduction to artificial intelligence reading material | revision lecture | knowledge representation lecture | semantic web lecture | knowledge representation practical | semantic web practical | artificial intelligence practical | introduction to artificial intelligence practical | knowledge representation reading material | knowledge representation notes | semantic web notes | artificial intelligence notes | introduction to artificial intelligence notes | neural network lecture | neural networks lecture | artificial neural networks lecture | neural network reading material | neural networks reading material | artificial neural networks reading material | neural network practical | neural networks practical | artificial neural networks practical | neural network viewing material | neural networks viewing material | artificial neural networks viewing material | artificial intelligence viewing material | introduction to artificial intelligence viewing material | swarm intelligence lecture | collective intelligence lecture | robot societies lecture | swarm intelligence tutorial | collective intelligence tutorial | robot societies tutorial | evolutionary algorithm test | genetic computation test | genetic programming test | introduction to artificial intelligence test | search test | problem solving test | semantic web test | neural network test | artificial neural networks test | g700 | ai | g700 lecture | ai lecture | g700 tutorial | ai tutorial | g700 example | ai example | g700 reading material | ai reading material | g700 practical | ai practical | g700 notes | ai notes | g700 viewing material | ai viewing material | g700 test | ai test | Computer science | I100

License

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