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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

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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

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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

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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

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

Description

Includes audio/video content: AV lectures. 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. Includes audio/video content: AV lectures. 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 | knowledge representation | knowledge representation | problem solving | problem solving | learning methods | learning methods | intelligent systems | intelligent systems | basic search | basic search | optimal search | optimal search | neural nets | neural nets | genetic algorithms | genetic algorithms | support vector machines | support vector machines | boosting | boosting | probabilistic inference | probabilistic inference

License

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

Description

This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms. This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.

Subjects

Introduces representations | techniques | and architectures used to build applied systems | Introduces representations | techniques | and architectures used to build applied systems | computational intelligence | computational intelligence | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | problem-solving paradigms | problem-solving paradigms | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | learning paradigms | learning paradigms | Speculations on the contributions of human vision and language systems to human intelligence | Speculations on the contributions of human vision and language systems to human intelligence | Meets with HST.947 spring only | Meets with HST.947 spring only | 4 Engineering Design Points | 4 Engineering Design Points

License

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16.892J Space System Architecture and Design (MIT) 16.892J Space System Architecture and Design (MIT)

Description

Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues. Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues.

Subjects

space system | space system | space system architecture | space system architecture | space architecting | space architecting | uncertainties | uncertainties | space policy | space policy | robustness | robustness | flexibility | flexibility | optimality | optimality | tradespace analysis | tradespace analysis | quality function deployment | quality function deployment | multi-attribute utility theory | multi-attribute utility theory | n-squared | n-squared | design structure matrix | design structure matrix | multi-attribution tradespace exploration | multi-attribution tradespace exploration | MATE | MATE | MATE-CON | MATE-CON | satellite | satellite | classes of space system | classes of space system | XTOS | XTOS | spacetug | spacetug | GINA | GINA | pareto fronts | pareto fronts | engineering design process | engineering design process | optimization methods | optimization methods | genetic algorithms | genetic algorithms | simulated annealing | simulated annealing | MMDOSA | MMDOSA | distributed space systems design optimization | distributed space systems design optimization | clarity test | clarity test | taxonomy of uncertainty | taxonomy of uncertainty | treatment of uncertainty | treatment of uncertainty | irreducible uncertainty | irreducible uncertainty | portfolio theory | portfolio theory | portfolio applications | portfolio applications | taxonomy of flexibility | taxonomy of flexibility | on-orbit servicing | on-orbit servicing | US national space policy | US national space policy | space policy heuristics | space policy heuristics | policy architectures | policy architectures | 16.892 | 16.892 | ESD.353 | ESD.353

License

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ESD.77 Multidisciplinary System Design Optimization (MIT) ESD.77 Multidisciplinary System Design Optimization (MIT)

Description

There is need for a rigorous, quantitative multidisciplinary design methodology that works with the non-quantitative and creative side of the design process in engineering systems. The goal of multidisciplinary systems design optimization is to create advanced and complex engineering systems that must be competitive not only in terms of performance, but also in terms of life-cycle value. The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context. Focus will be equally strong on all three aspects of the problem: (i) the multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. There is need for a rigorous, quantitative multidisciplinary design methodology that works with the non-quantitative and creative side of the design process in engineering systems. The goal of multidisciplinary systems design optimization is to create advanced and complex engineering systems that must be competitive not only in terms of performance, but also in terms of life-cycle value. The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context. Focus will be equally strong on all three aspects of the problem: (i) the multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization.

Subjects

systems engineering | systems engineering | optimization | optimization | product design | product design | multidisciplinary design optimization | multidisciplinary design optimization | subsystem identification | subsystem identification | heuristic search methods | heuristic search methods | genetic algorithms | genetic algorithms | simulated annealing | simulated annealing | Pareto optimality | Pareto optimality | design for value | design for value

License

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HST.947 Medical Artificial Intelligence (MIT) HST.947 Medical Artificial Intelligence (MIT)

Description

This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers. This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers.

Subjects

Introduces representations | techniques | and architectures used to build applied systems | Introduces representations | techniques | and architectures used to build applied systems | computational intelligence | computational intelligence | rule chaining | rule chaining | heuristic search | heuristic search | constraint propagation | constraint propagation | constrained search | constrained search | inheritance | inheritance | problem-solving paradigms | problem-solving paradigms | identification trees | identification trees | neural nets | neural nets | genetic algorithms | genetic algorithms | learning paradigms | learning paradigms | Speculations on the contributions of human vision and language systems to human intelligence | Speculations on the contributions of human vision and language systems to human intelligence | Meets with HST.947 spring only | Meets with HST.947 spring only | 4 Engineering Design Points | 4 Engineering Design Points | artificial intelligence | artificial intelligence | applied systems | applied systems | human intelligence | human intelligence | knowledge representation | knowledge representation | intelligent systems | intelligent systems | diagnosis | diagnosis | clinical simulation | clinical simulation | genomics | genomics | proteomics | proteomics | bioinformatics | bioinformatics

License

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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

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2.A35 Biomimetic Principles and Design (MIT) 2.A35 Biomimetic Principles and Design (MIT)

Description

Biomimetics is based on the belief that nature, at least at times, is a good engineer. Biomimesis is the scientific method of learning new principles and processes based on systematic study, observation and experimentation with live animals and organisms. This Freshman Advising Seminar on the topic is a way for freshmen to explore some of MIT's richness and learn more about what they may want to study in later years. Biomimetics is based on the belief that nature, at least at times, is a good engineer. Biomimesis is the scientific method of learning new principles and processes based on systematic study, observation and experimentation with live animals and organisms. This Freshman Advising Seminar on the topic is a way for freshmen to explore some of MIT's richness and learn more about what they may want to study in later years.

Subjects

biomimetics | biomimetics | biomimicry | biomimicry | biomimesis | biomimesis | nature | nature | reverse engineering | reverse engineering | bionics | bionics | adaptation | adaptation | genetic algorithms | genetic algorithms | politics | politics | design | design | imitate | imitate | robot | robot | robotics | robotics | robotuna | robotuna | fluid mechanics | fluid mechanics | fish | fish | swim | swim | submarine | submarine | complexity | complexity

License

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Non-Standard Computing

Description

Inspired by reality-based computing from the natural world, this course covers several unconventional computational methods and theories, such as quantum computation, DNA and molecular computation, genetic algorithms, self-organizing networks, and cellular automata. Note: for this course, it will be quite helpful to have a working knowledge of cellular biology (available from the Saylor Foundation’s BIO301). This free course may be completed online at any time. See course site for detailed overview and learning outcomes. (Computer Science 411)

Subjects

computer science | dna | biocomputers | genetic algorithms | cellular automata | neural networks | quantum computing | Computer science | I100

License

Attribution 2.0 UK: England & Wales Attribution 2.0 UK: England & Wales http://creativecommons.org/licenses/by/2.0/uk/ http://creativecommons.org/licenses/by/2.0/uk/

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Non-Standard Computing

Description

Inspired by reality-based computing from the natural world, this course covers several unconventional computational methods and theories, such as quantum computation, DNA and molecular computation, genetic algorithms, self-organizing networks, and cellular automata. Note: for this course, it will be quite helpful to have a working knowledge of cellular biology (available from the Saylor Foundation’s BIO301). This free course may be completed online at any time. See course site for detailed overview and learning outcomes. (Computer Science 411)

Subjects

computer science | dna | biocomputers | genetic algorithms | cellular automata | neural networks | quantum computing | Computer science | I100

License

Attribution 2.0 UK: England & Wales Attribution 2.0 UK: England & Wales http://creativecommons.org/licenses/by/2.0/uk/ http://creativecommons.org/licenses/by/2.0/uk/

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16.410 Principles of Autonomy and Decision Making (MIT)

Description

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

Subjects

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

License

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

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6.034 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.

Subjects

artificial intelligence | applied systems | rule chaining | heuristic search | constraint propagation | constrained search | inheritance | identification trees | neural nets | genetic algorithms | human intelligence | knowledge representation | 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 https://ocw.mit.edu/terms/index.htm

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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.

Subjects

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

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2.A35 Biomimetic Principles and Design (MIT)

Description

Biomimetics is based on the belief that nature, at least at times, is a good engineer. Biomimesis is the scientific method of learning new principles and processes based on systematic study, observation and experimentation with live animals and organisms. This Freshman Advising Seminar on the topic is a way for freshmen to explore some of MIT's richness and learn more about what they may want to study in later years.

Subjects

biomimetics | biomimicry | biomimesis | nature | reverse engineering | bionics | adaptation | genetic algorithms | politics | design | imitate | robot | robotics | robotuna | fluid mechanics | fish | swim | submarine | complexity

License

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16.892J Space System Architecture and Design (MIT)

Description

Space System Architecture and Design incorporates lectures, readings and discussion on topics in the architecting of space systems. The class reviews existing space system architectures and the classical methods of designing them. Sessions focus on multi-attribute utility theory as a new design paradigm for space systems, when combined with integrated concurrent engineering and efficient searches of large architectural tradespaces. Designing for flexibility and uncertainty is considered, as are policy and product development issues.

Subjects

space system | space system architecture | space architecting | uncertainties | space policy | robustness | flexibility | optimality | tradespace analysis | quality function deployment | multi-attribute utility theory | n-squared | design structure matrix | multi-attribution tradespace exploration | MATE | MATE-CON | satellite | classes of space system | XTOS | spacetug | GINA | pareto fronts | engineering design process | optimization methods | genetic algorithms | simulated annealing | MMDOSA | distributed space systems design optimization | clarity test | taxonomy of uncertainty | treatment of uncertainty | irreducible uncertainty | portfolio theory | portfolio applications | taxonomy of flexibility | on-orbit servicing | US national space policy | space policy heuristics | policy architectures | 16.892 | ESD.353

License

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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

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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.

Subjects

artificial intelligence | knowledge representation | problem solving | learning methods | intelligent systems | basic search | optimal search | neural nets | genetic algorithms | support vector machines | boosting | probabilistic inference

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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ESD.77 Multidisciplinary System Design Optimization (MIT)

Description

There is need for a rigorous, quantitative multidisciplinary design methodology that works with the non-quantitative and creative side of the design process in engineering systems. The goal of multidisciplinary systems design optimization is to create advanced and complex engineering systems that must be competitive not only in terms of performance, but also in terms of life-cycle value. The objective of the course is to present tools and methodologies for performing system optimization in a multidisciplinary design context. Focus will be equally strong on all three aspects of the problem: (i) the multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization.

Subjects

systems engineering | optimization | product design | multidisciplinary design optimization | subsystem identification | heuristic search methods | genetic algorithms | simulated annealing | Pareto optimality | design for value

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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HST.947 Medical Artificial Intelligence (MIT)

Description

This course provides an intensive introduction to artificial intelligence and its applications to problems of medical diagnosis, therapy selection, and monitoring and learning from databases. It meets with lectures and recitations of 6.034 Artificial Intelligence, whose material is supplemented by additional medical-specific readings in a weekly discussion session. Students are responsible for completing all homework assignments in 6.034 and for additional problems and/or papers.

Subjects

Introduces representations | techniques | and architectures used to build applied systems | computational intelligence | rule chaining | heuristic search | constraint propagation | constrained search | inheritance | problem-solving paradigms | identification trees | neural nets | genetic algorithms | learning paradigms | Speculations on the contributions of human vision and language systems to human intelligence | Meets with HST.947 spring only | 4 Engineering Design Points | artificial intelligence | applied systems | human intelligence | knowledge representation | intelligent systems | diagnosis | clinical simulation | genomics | proteomics | bioinformatics

License

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6.034 Artificial Intelligence (MIT)

Description

This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms.

Subjects

Introduces representations | techniques | and architectures used to build applied systems | computational intelligence | rule chaining | heuristic search | constraint propagation | constrained search | inheritance | problem-solving paradigms | identification trees | neural nets | genetic algorithms | learning paradigms | Speculations on the contributions of human vision and language systems to human intelligence | Meets with HST.947 spring only | 4 Engineering Design Points

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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