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Readme file for Structured Systems Analysis

Description

This readme file contains details of links to all the Readme file for Structured Systems Analysis module's material held on Jorum and information about the module as well.

Subjects

ukoer | current logical data flow diagram example | current logical data flow diagram exercise | current logical data flow diagram teaching guide | current logical data flow diagram video lecture | current logical data flow diagram | current logical data flow diagrams example | current logical data flow diagrams exercise | current logical data flow diagrams teaching guide | current logical data flow diagrams video lecture | current logical data flow diagrams | current logical dfd example | current logical dfd exercise | current logical dfd teaching guide | current logical dfd video lecture | current logical dfd | current logical dfds example | current logical dfds exercise | current logical dfds lecture | current logical dfds teaching guide | data dictionary example | data dictionary exercise | data dictionary lecture | data dictionary reading material | data dictionary teaching guide | data dictionary video lecture | data dictionary | data example | data exercise | data flow diagram example | data flow diagram exercise | data flow diagram reading material | data flow diagram teaching guide | data flow diagram video lecture | data flow diagram | data flow diagrams example | data flow diagrams exercise | data flow diagrams reading material | data flow diagrams teaching guide | data flow diagrams video lecture | data flow diagrams | data reading material | data teaching guide | data video lecture | data | decision table and tree example | decision table and tree exercise | decision table and tree reading material | decision table and tree teaching guide | decision table and tree video lecture | decision table and tree | decision table example | decision table exercise | decision table reading material | decision table teaching guide | decision table video lecture | decision table | decision tables and trees example | decision tables and trees lecture | decision tables and trees reading material | decision tables example | decision tables exercise | decision tables reading material | decision tables teaching guide | decision tables video lecture | decision tables | decision tree example | decision tree exercise | decision tree reading material | decision tree teaching guide | decision tree video lecture | decision tree | decision trees and decision tables exercise | decision trees and decision tables teaching guide | decision trees and decision tables video lecture | decision trees and decision tables | decision trees example | decision trees exercise | decision trees reading material | decision trees teaching guide | decision trees video lecture | decision trees | dfd example | dfd exercise | dfd reading material | dfd teaching guide | dfd video lecture | dfd | dfds example | dfds exercise | dfds reading material | dfds teaching guide | dfds video lecture | dfds | exploding data flow diagrams example | exploding data flow diagrams exercise | exploding data flow diagrams reading material | exploding data flow diagrams teaching guide | exploding data flow diagrams | exploding dfd example | exploding dfd exercise | exploding dfd reading material | exploding dfd teaching guide | exploding dfd | logical data flow diagram example | logical data flow diagram exercise | logical data flow diagram reading material | logical data flow diagram teaching guide | logical data flow diagram video lecture | logical data flow diagram | logical data flow diagrams example | logical data flow diagrams exercise | logical data flow diagrams teaching guide | logical data flow diagrams video lecture | logical data flow diagrams | logical dfd example | logical dfd exercise | logical dfd reading material | logical dfd teaching guide | logical dfd video lecture | logical dfd | logical dfds example | logical dfds exercise | logical dfds reading material | logical dfds teaching guide | logical dfds video lecture | logical dfds | project management practical | project management reading material | project management task guide | project management teaching guide | project management | quality management | quality managment reading material | quality managment task guide | required logical data flow diagram example | required logical data flow diagram exercise | required logical data flow diagram reading material | required logical data flow diagram teaching guide | required logical data flow diagram video lecture | required logical data flow diagram | required logical data flow diagrams example | required logical data flow diagrams exercise | required logical data flow diagrams reading material | required logical data flow diagrams teaching guide | required logical data flow diagrams video lecture | required logical data flow diagrams | required logical dfd example | required logical dfd exercise | required logical dfd reading material | required logical dfd teaching guide | required logical dfd video lecture | required logical dfd | required logical dfds example | required logical dfds exercise | required logical dfds lecture | required logical dfds reading material | required logical dfds teaching guide | structured chart example | structured chart exercise | structured chart reading material | structured chart teaching guide | structured chart video lecture | structured chart | structured charts example | structured charts exercise | structured charts lecture | structured charts reading material | structured charts teaching guide | structured charts video lecture | structured charts | structured english example | structured english exercise | structured english lecture | structured english teaching guide | structured english video lecture | structured english | structured system analysis example | structured system analysis exercise | structured system analysis lecture | structured system analysis practical | structured system analysis reading material | structured system analysis task guide | structured system analysis teaching guide | structured system analysis video lecture | structured system analysis | structured systems analysis example | structured systems analysis exercise | structured systems analysis lecture | structured systems analysis practical | structured systems analysis reading material | structured systems analysis task guide | structured systems analysis teaching guide | structured systems analysis video lecture | structured systems analysis | structured walkthroughs reading material | system analysis example | system analysis exercise | system analysis lecture | system analysis practical | system analysis reading material | system analysis task guide | system analysis teaching guide | system analysis video lecture | system analysis | systems analysis example | systems analysis exercise | systems analysis lecture | systems analysis practical | systems analysis reading material | systems analysis task guide | systems analysis teaching guide | systems analysis video lecture | systems analysis | techniques in methods lecture | techniques in methods teaching guide | techniques in methods | uml | univeral modelling language lecture | univeral modelling language | universal modeling language lecture | universal modeling language | current logical dfds video lecture | current logical dfds | exploding dfds example | exploding dfds exercise | exploding dfds reading material | exploding dfds teaching guide | exploding dfds | levelling dfds example | levelling dfds exercise | levelling dfds reading material | levelling dfds teaching guide | levelling dfds | required logical dfds video lecture | required logical dfds | uml lecture | Computer science | I100

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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Structured Systems Analysis - Decision Tables and Trees

Description

This video lecture forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

decision tables and trees lecture | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees video lecture | decision tables video lecture | decision trees and decision tables video lecture | systems analysis video lecture | structured systems analysis video lecture | system analysis video lecture | structured system analysis video lecture | decision table video lecture | decision tree video lecture | decision table and tree video lecture | Computer science | I100

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) 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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18.997 Topics in Combinatorial Optimization (MIT) 18.997 Topics in Combinatorial Optimization (MIT)

Description

In this graduate-level course, we will be covering advanced topics in combinatorial optimization. We will start with non-bipartite matchings and cover many results extending the fundamental results of matchings, flows and matroids. The emphasis is on the derivation of purely combinatorial results, including min-max relations, and not so much on the corresponding algorithmic questions of how to find such objects. The intended audience consists of Ph.D. students interested in optimization, combinatorics, or combinatorial algorithms. In this graduate-level course, we will be covering advanced topics in combinatorial optimization. We will start with non-bipartite matchings and cover many results extending the fundamental results of matchings, flows and matroids. The emphasis is on the derivation of purely combinatorial results, including min-max relations, and not so much on the corresponding algorithmic questions of how to find such objects. The intended audience consists of Ph.D. students interested in optimization, combinatorics, or combinatorial algorithms.

Subjects

combinatorial optimization | combinatorial optimization | Ear decompositions | Ear decompositions | Nonbipartite matching | Nonbipartite matching | Gallai-Milgram and Bessy-Thomasse theorems on partitioning/covering graphs by directed paths/cycles | Gallai-Milgram and Bessy-Thomasse theorems on partitioning/covering graphs by directed paths/cycles | Minimization of submodular functions | Minimization of submodular functions | Matroid intersection | Matroid intersection | Polymatroid intersection | Polymatroid intersection | Jump systems | Jump systems | Matroid union | Matroid union | Matroid matching | path matchings | Matroid matching | path matchings | Packing trees and arborescences | Packing trees and arborescences | Packing directed cuts and the Lucchesi-Younger theorem | Packing directed cuts and the Lucchesi-Younger theorem | Submodular flows and the Edmonds-Giles theorem | Submodular flows and the Edmonds-Giles theorem | Graph orientation | Graph orientation | Connectivity tree and connectivity augmentation | Connectivity tree and connectivity augmentation | Multicommodity flows | Multicommodity flows | Connectivity tree | Connectivity tree | connectivity augmentation | connectivity augmentation | Gallai-Milgram Theorem | Gallai-Milgram Theorem | Bessy-Thomasse Theorem | Bessy-Thomasse Theorem | paritioning graphs | paritioning graphs | covering graphs | covering graphs | directed paths | directed paths | directed cycles | directed cycles | matroid matching | matroid matching | path matching | path matching | packing directed cuts | packing directed cuts | Luchessi-Younger Theorem | Luchessi-Younger Theorem | packing trees | packing trees | arborescences | arborescences | submodular flows | submodular flows | Edmonds-Giles Theorem | Edmonds-Giles Theorem

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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Structured Systems Analysis - Decision Tables and Trees

Description

This teaching guide forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees teaching guide | decision tables teaching guide | decision trees and decision tables teaching guide | systems analysis teaching guide | structured systems analysis teaching guide | system analysis teaching guide | structured system analysis teaching guide | decision table teaching guide | decision tree teaching guide | decision table and tree teaching guide | Computer science | I100

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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Structured Systems Analysis - Decision Tables and Trees

Description

This example forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision tables and trees example | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees example | decision tables example | systems analysis example | structured systems analysis example | system analysis example | structured system analysis example | decision table example | decision tree example | decision table and tree example | Computer science | I100

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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Structured Systems Analysis - Decision Tables and Trees

Description

This reading material forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision tables and trees reading material | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees reading material | decision tables reading material | systems analysis reading material | structured systems analysis reading material | system analysis reading material | structured system analysis reading material | decision table reading material | decision tree reading material | decision table and tree reading material | Computer science | I100

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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Structured Systems Analysis - Decision Tables and Trees

Description

This exercise forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees exercise | decision tables exercise | decision trees and decision tables exercise | systems analysis exercise | structured systems analysis exercise | system analysis exercise | structured system analysis exercise | decision table exercise | decision tree exercise | decision table and tree exercise | Computer science | I100

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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Structured Systems Analysis - Decision Tables and Trees

Description

This exercise forms part of the "Decision Tables and Trees" topic in the Structured Systems Analysis module.

Subjects

ukoer | decision trees | decision tables | decision trees and decision tables | systems analysis | structured systems analysis | system analysis | structured system analysis | decision table | decision tree | decision table and tree | decision trees exercise | decision tables exercise | decision trees and decision tables exercise | systems analysis exercise | structured systems analysis exercise | system analysis exercise | structured system analysis exercise | decision table exercise | decision tree exercise | decision table and tree exercise | Computer science | I100

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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Riverside, Calif. By Mr. Taylor Riverside, Calif. By Mr. Taylor

Description

Subjects

california | california | trees | trees | palmtrees | palmtrees | roads | roads | carriages | carriages | uswest | uswest

License

No known copyright restrictions

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Unidentified residence - Tallahassee Unidentified residence - Tallahassee

Description

Subjects

trees | trees | homes | homes | florida | florida | tallahassee | tallahassee | oaktrees | oaktrees | dilapidations | dilapidations | woodenhomes | woodenhomes

License

No known copyright restrictions

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6.034 Artificial Intelligence (MIT) 6.034 Artificial Intelligence (MIT)

Description

6.034 is the header course for the department's "Artificial Intelligence and Applications" concentration. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective. 6.034 is the header course for the department's "Artificial Intelligence and Applications" concentration. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Subjects

artificial intelligence | artificial intelligence | applied systems | applied systems | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systems

License

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

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12.007 Geobiology (MIT) 12.007 Geobiology (MIT)

Description

The interactive Earth system: Biology in geologic, environmental and climate change throughout Earth history. Since life began it has continually shaped and re-shaped the atmosphere, hydrosphere, cryosphere and the solid earth. Introduces the concept of "life as a geological agent" and examines the interaction between biology and the earth system during the roughly 4 billion years since life first appeared. Topics include the origin of the solar system and the early Earth atmosphere; the origin and evolution of life and its influence on climate up through and including the modern age and the problem of global warming; the global carbon cycle; and astrobiology. The interactive Earth system: Biology in geologic, environmental and climate change throughout Earth history. Since life began it has continually shaped and re-shaped the atmosphere, hydrosphere, cryosphere and the solid earth. Introduces the concept of "life as a geological agent" and examines the interaction between biology and the earth system during the roughly 4 billion years since life first appeared. Topics include the origin of the solar system and the early Earth atmosphere; the origin and evolution of life and its influence on climate up through and including the modern age and the problem of global warming; the global carbon cycle; and astrobiology.

Subjects

Big Bang | Big Bang | carbon cycle | carbon cycle | geobiochemistry | geobiochemistry | Solar System formation | Solar System formation | evolution | evolution | isotopic analysis: climate | isotopic analysis: climate | climate change | climate change | Snowball earth | Snowball earth | mesozoic | mesozoic | proterozoic | proterozoic | mass extinctions | mass extinctions | paleoclimate | paleoclimate | antiquity of life | antiquity of life | carbon dating | carbon dating | origin of life | origin of life | phylogenic trees | phylogenic 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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6.034 Artificial Intelligence (MIT) 6.034 Artificial Intelligence (MIT)

Description

This course introduces students to the basic knowledge representation, problem solving, and learning methods of  artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Technical RequirementsJava® plug-in software is required to run the .jar files found on this course site.Java® is a trademark or registered trademark of Sun Microsystems, Inc. in the United States and other countries. This course introduces students to the basic knowledge representation, problem solving, and learning methods of  artificial intelligence. Upon completion of 6.034, students should be able to: develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.Technical RequirementsJava® plug-in software is required to run the .jar files found on this course site.Java® is a trademark or registered trademark of Sun Microsystems, Inc. in the United States and other countries.

Subjects

artificial intelligence | artificial intelligence | applied systems | applied systems | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systems

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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6.046J Introduction to Algorithms (SMA 5503) (MIT) 6.046J Introduction to Algorithms (SMA 5503) (MIT)

Description

This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5503 (Analysis and Design of Algorithms). This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5503 (Analysis and Design of Algorithms).

Subjects

algorithms | algorithms | efficient algorithms | efficient algorithms | sorting | sorting | search trees | search trees | heaps | heaps | hashing | hashing | divide-and-conquer | divide-and-conquer | dynamic programming | dynamic programming | amortized analysis | amortized analysis | graph algorithms | graph algorithms | shortest paths | shortest paths | network flow | network flow | computational geometry | computational geometry | number-theoretic algorithms | number-theoretic algorithms | polynomial and matrix calculations | polynomial and matrix calculations | caching | caching | parallel computing | parallel computing | SMA 5503 | SMA 5503 | 6.046 | 6.046

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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3.A24 Freshman Seminar: The Engineering of Birds (MIT) 3.A24 Freshman Seminar: The Engineering of Birds (MIT)

Description

Why are things in nature shaped the way they are? How do birds fly? Why do bird nests look the way they do? How do woodpeckers peck? These are the types of questions Dr. Lorna Gibson's freshman seminar at MIT has been investigating. We invite you to explore with us.Questions such as these are the subject of biomimetic research. When engineers copy the shapes found in nature we call it Biomimetics. The word biomimic comes from bio, as in biology and mimetic, which means to copy.Technical RequirementsRealOne™ Player software is required to run the .rm files on this page. Why are things in nature shaped the way they are? How do birds fly? Why do bird nests look the way they do? How do woodpeckers peck? These are the types of questions Dr. Lorna Gibson's freshman seminar at MIT has been investigating. We invite you to explore with us.Questions such as these are the subject of biomimetic research. When engineers copy the shapes found in nature we call it Biomimetics. The word biomimic comes from bio, as in biology and mimetic, which means to copy.Technical RequirementsRealOne™ Player software is required to run the .rm files on this page.

Subjects

freshman seminar | freshman seminar | service learning | service learning | biomimetic research | biomimetic research | Biomimetics | Biomimetics | biology | biology | mimetic | mimetic | physics | physics | nature | nature | natural engineering | natural engineering | wood | wood | trees | 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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6.046J Introduction to Algorithms (MIT) 6.046J Introduction to Algorithms (MIT)

Description

This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing. This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.

Subjects

algorithms | algorithms | efficient algorithms | efficient algorithms | sorting | sorting | search trees | search trees | heaps | heaps | hashing | hashing | divide-and-conquer | divide-and-conquer | dynamic programming | dynamic programming | amortized analysis | amortized analysis | graph algorithms | graph algorithms | shortest paths | shortest paths | network flow | network flow | computational geometry | computational geometry | number-theoretic algorithms | number-theoretic algorithms | polynomial and matrix calculations | polynomial and matrix calculations | caching | caching | parallel computing | parallel computing | 6.046 | 6.046 | 18.410 | 18.410

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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3.A24 Freshman Seminar: The Engineering of Trees (MIT) 3.A24 Freshman Seminar: The Engineering of Trees (MIT)

Description

Why are things in nature shaped the way they are? Why can't trees grow taller than they are? Why is grass skinny and hollow? Why are some leaves full of holes? These are the types of questions Dr. Lorna Gibson's& freshman seminar at MIT has been investigating. We invite you to explore with us.Questions such as these are the subject of biomimetic research. When engineers copy the shapes found in nature we call it Biomimetics. The word biomimic comes from bio, as in biology and mimetic, which means to copy. Why are things in nature shaped the way they are? Why can't trees grow taller than they are? Why is grass skinny and hollow? Why are some leaves full of holes? These are the types of questions Dr. Lorna Gibson's& freshman seminar at MIT has been investigating. We invite you to explore with us.Questions such as these are the subject of biomimetic research. When engineers copy the shapes found in nature we call it Biomimetics. The word biomimic comes from bio, as in biology and mimetic, which means to copy.

Subjects

freshman seminar | freshman seminar | service learning | service learning | biomimetic research | biomimetic research | Biomimetics | Biomimetics | biology | biology | mimetic | mimetic | physics | physics | nature | nature | natural engineering | natural engineering | wood | wood | trees | 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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13.472J Computational Geometry (MIT) 13.472J Computational Geometry (MIT)

Description

Topics in surface modeling: b-splines, non-uniform rational b-splines, physically based deformable surfaces, sweeps and generalized cylinders, offsets, blending and filleting surfaces. Non-linear solvers and intersection problems. Solid modeling: constructive solid geometry, boundary representation, non-manifold and mixed-dimension boundary representation models, octrees. Robustness of geometric computations. Interval methods. Finite and boundary element discretization methods for continuum mechanics problems. Scientific visualization. Variational geometry. Tolerances. Inspection methods. Feature representation and recognition. Shape interrogation for design, analysis, and manufacturing. Involves analytical and programming assignments. Topics in surface modeling: b-splines, non-uniform rational b-splines, physically based deformable surfaces, sweeps and generalized cylinders, offsets, blending and filleting surfaces. Non-linear solvers and intersection problems. Solid modeling: constructive solid geometry, boundary representation, non-manifold and mixed-dimension boundary representation models, octrees. Robustness of geometric computations. Interval methods. Finite and boundary element discretization methods for continuum mechanics problems. Scientific visualization. Variational geometry. Tolerances. Inspection methods. Feature representation and recognition. Shape interrogation for design, analysis, and manufacturing. Involves analytical and programming assignments.

Subjects

surface modeling | surface modeling | b-splines | b-splines | deformable surfaces | deformable surfaces | generalized cylinders | generalized cylinders | offsets | offsets | filleting surfaces | filleting surfaces | Non-linear solvers and intersection problems | Non-linear solvers and intersection problems | Solid modeling | Solid modeling | boundary representation | boundary representation | non-manifold and mixed-dimension boundary representation models | non-manifold and mixed-dimension boundary representation models | octrees | octrees | Interval methods | Interval methods | discretization methods | discretization methods | Scientific visualization | Scientific visualization | Variational geometry | Variational geometry | Tolerances | Tolerances | Inspection methods | Inspection methods | Shape interrogation | Shape interrogation | 2.158J | 2.158J | 1.128J | 1.128J | 16.940J | 16.940J | 13.472 | 13.472 | 2.158 | 2.158 | 1.128 | 1.128 | 16.940 | 16.940

License

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6.034 Artificial Intelligence (MIT) 6.034 Artificial Intelligence (MIT)

Description

6.034 introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Topics covered include: applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms, as well as applications of decision trees, neural nets, SVMs and other learning paradigms. 6.034 introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Topics covered include: applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms, as well as applications of decision trees, neural nets, SVMs and other learning paradigms.

Subjects

artificial intelligence | artificial intelligence | applied systems | applied systems | intelligence | intelligence | rule chaining | rule chaining | heuristic search | heuristic search | logic | logic | constraint propagation | constraint propagation | constrained search | constrained search | decision trees | decision trees | neural nets | neural nets | SVMs | SVMs | problem-solving paradigms | problem-solving paradigms

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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8.592 Statistical Physics in Biology (MIT) 8.592 Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; Considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.Technical RequirementsAny number of biological sequence comparison software tools can be used to import the .fna files found on this course site. Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; Considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.Technical RequirementsAny number of biological sequence comparison software tools can be used to import the .fna files found on this course site.

Subjects

Bioinformatics | Bioinformatics | DNA | DNA | gene finding | gene finding | sequence comparison | sequence comparison | phylogenetic trees | phylogenetic trees | biopolymers | biopolymers | DNA double helix | DNA double helix | secondary structure of RNA | secondary structure of RNA | protein folding | protein folding | protein motors | membranes | protein motors | membranes | cellular networks | cellular networks | neural networks | neural networks | evolution | evolution | statistical physics | statistical physics | molecular biology | molecular biology | deoxyribonucleic acid | deoxyribonucleic acid | genes | genes | genetics | genetics | gene sequencing | gene sequencing | phylogenetics | phylogenetics | double helix | double helix | RNA | RNA | ribonucleic acid | ribonucleic acid | force | force | motion | motion | packaging | packaging | protein motors | protein motors | membranes | membranes | biochemistry | biochemistry | genome | genome | optimization | optimization | partitioning | partitioning | pattern recognition | pattern recognition | collective behavior | collective behavior

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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s wood culture

Description

Part of the Future of Crops lecture series delivered at the Oxford Botanic Gardens. Trees, woodlands and their product, wood, surround our lives. In Britain today, people that access or connect with woodlands do so through a society that cherishes the beauty of the treescape and the wildlife it supports. A new dawn is breaking for forestry. Can we manage forests for carbon, grow fibre for wood heat and energy, and adapt to climate change, while continuing to meet existing objectives from UK forests? The forestry sector needs to engage with society. The Sylva Foundation's OneOak project aims to do this through science and art, focussing on the full life story of one oak tree. Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Subjects

ecology | gardening | food | botanic gardens | woodland | trees | one oak project | ecology | gardening | food | botanic gardens | woodland | trees | one oak project | 2010-12-29

License

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

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Reflecting pool and landscaped grounds at Kilearn Gardens State Park in Tallahassee, Florida

Description

Subjects

trees | plants | gardens | florida | landscaping | lakes | palmtrees | 1950s | dogwood | tallahassee | redbud | reflectingpools | leoncounty | lakehall | floridastateparks | maclaygardensstatepark | statelibraryandarchivesofflorida | tallahasseedemocrattallahasseefloridanewspaper | vision:mountain=0616 | vision:outdoor=097 | vision:sky=0548 | kilearngardensstatepark

License

No known copyright restrictions

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

Description

Includes audio/video content: AV lectures. 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. Includes audio/video content: AV lectures. 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 | data structures | data structures | algorithm performance | algorithm performance | algorithm analysis | algorithm analysis | sorting | sorting | trees | trees | hashing | hashing | numerics | numerics | graphs | graphs | shortest paths | shortest paths | dynamic programming | dynamic programming | Python | Python

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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