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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 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 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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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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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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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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15.053 Optimization Methods in Management Science (MIT) 15.053 Optimization Methods in Management Science (MIT)

Description

This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice. This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice.

Subjects

optimization methods | optimization methods | management science | management science | theory | theory | algorithms | algorithms | applications | applications | linear programming | linear programming | network optimization | network optimization | integer programming | integer programming | decision trees | decision trees | logistics | logistics | manufacturing | manufacturing | transportation | transportation | marketing | marketing | project management | project management | finance | finance

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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15.053 Optimization Methods in Management Science (MIT) 15.053 Optimization Methods in Management Science (MIT)

Description

15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance. 15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance.

Subjects

optimization methods | optimization methods | management science | management science | theory | theory | algorithms | algorithms | applications | applications | linear programming | linear programming | network optimization | network optimization | integer programming | integer programming | decision trees | decision trees | logistics | logistics | manufacturing | manufacturing | transportation | transportation | marketing | marketing | project management | project management | finance | finance

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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MAS.622J Pattern Recognition and Analysis (MIT) MAS.622J Pattern Recognition and Analysis (MIT)

Description

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class. This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

Subjects

MAS.622 | MAS.622 | 1.126 | 1.126 | pattern recognition | pattern recognition | feature detection | feature detection | classification | classification | probability theory | probability theory | pattern analysis | pattern analysis | conditional probability | conditional probability | bayes rule | bayes rule | random vectors | decision theory | random vectors | decision theory | ROC curves | ROC curves | likelihood ratio test | likelihood ratio test | fisher discriminant | fisher discriminant | template-based recognition | template-based recognition | feature extraction | feature extraction | eigenvector and multilinear analysis | eigenvector and multilinear analysis | linear discriminant | linear discriminant | perceptron learning | perceptron learning | optimization by gradient descent | optimization by gradient descent | support vecotr machines | support vecotr machines | K-nearest-neighbor classification | K-nearest-neighbor classification | parzen estimation | parzen estimation | unsupervised learning | unsupervised learning | clustering | clustering | vector quantization | vector quantization | K-means | K-means | Expectation-Maximization | Expectation-Maximization | Hidden markov models | Hidden markov models | viterbi algorithm | viterbi algorithm | Baum-Welch algorithm | Baum-Welch algorithm | linear dynamical systems | linear dynamical systems | Kalman filtering | Kalman filtering | Bayesian networks | Bayesian networks | decision trees | decision trees | reinforcement learning | reinforcement learning | genetic algorithms | genetic algorithms

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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MAS.622J Pattern Recognition and Analysis (MIT)

Description

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

Subjects

MAS.622 | 1.126 | pattern recognition | feature detection | classification | probability theory | pattern analysis | conditional probability | bayes rule | random vectors | decision theory | ROC curves | likelihood ratio test | fisher discriminant | template-based recognition | feature extraction | eigenvector and multilinear analysis | linear discriminant | perceptron learning | optimization by gradient descent | support vecotr machines | K-nearest-neighbor classification | parzen estimation | unsupervised learning | clustering | vector quantization | K-means | Expectation-Maximization | Hidden markov models | viterbi algorithm | Baum-Welch algorithm | linear dynamical systems | Kalman filtering | Bayesian networks | decision trees | reinforcement learning | genetic algorithms

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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15.053 Optimization Methods in Management Science (MIT)

Description

15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance.

Subjects

optimization methods | management science | theory | algorithms | applications | linear programming | network optimization | integer programming | decision trees | logistics | manufacturing | transportation | marketing | project management | finance

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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6.0002 Introduction to Computational Thinking and Data Science (MIT)

Description

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

Subjects

Python 3.5 | Python | machine learning | knapsack problem | greedy algorithm | optimization | weights | models | computational thinking | data science | dynamic programming | recursion | exponential time | stochastic | random | probability | independent variables | dependent variables | monte carlo simulation | simulation | population sampling | law of large numbers | variance | confidence interval | empirical rule | standard deviation | central limit theorem | bias | error distribution | sampling | error bars | numpy | scipy | matplotlib | pylab | python | plotting | graphing | supervised learning | computer modelling | signal-to-noise | feature vectors | classification model | regression model | classification | classifier | nearest neighbors | feature scaling | decision trees | entropy | training data | clustering | cluster analysis | unsupervised learning | objective function | dendogram | statistical fallacy | systematic errors | correlation and causation | misleading statistics | GIGO | axis truncating | extrapolation | data enhancement | Texas Sharpshooter Fallacy

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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15.053 Optimization Methods in Management Science (MIT)

Description

This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice.

Subjects

optimization methods | management science | theory | algorithms | applications | linear programming | network optimization | integer programming | decision trees | logistics | manufacturing | transportation | marketing | project management | finance

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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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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15.053 Optimization Methods in Management Science (MIT)

Description

15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance.

Subjects

optimization methods | management science | theory | algorithms | applications | linear programming | network optimization | integer programming | decision trees | logistics | manufacturing | transportation | marketing | project management | finance

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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15.053 Optimization Methods in Management Science (MIT)

Description

15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance.

Subjects

optimization methods | management science | theory | algorithms | applications | linear programming | network optimization | integer programming | decision trees | logistics | manufacturing | transportation | marketing | project management | finance

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)

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.

Subjects

artificial intelligence | applied systems | intelligence | rule chaining | heuristic search | logic | constraint propagation | constrained search | decision trees | neural nets | SVMs | 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 https://ocw.mit.edu/terms/index.htm

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15.053 Optimization Methods in Management Science (MIT)

Description

15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance.

Subjects

optimization methods | management science | theory | algorithms | applications | linear programming | network optimization | integer programming | decision trees | logistics | manufacturing | transportation | marketing | project management | finance

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

Site sourced from

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

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15.053 Optimization Methods in Management Science (MIT)

Description

15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance.

Subjects

optimization methods | management science | theory | algorithms | applications | linear programming | network optimization | integer programming | decision trees | logistics | manufacturing | transportation | marketing | project management | finance

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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http://ocw.mit.edu/rss/all/mit-allspanishcourses.xml

Attribution

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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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https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

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