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6.685 Electric Machines (MIT) 6.685 Electric Machines (MIT)

Description

6.685 explores concepts in electromechanics, using electric machinery as examples. It teaches an understanding of principles and analysis of electromechanical systems. By the end of the course, students are capable of doing electromechanical design of the major classes of rotating and linear electric machines, and have an understanding of the principles of the energy conversion parts of Mechatronics. In addition to design, students learn how to estimate the dynamic parameters of electric machines and understand what the implications of those parameters are on the performance of systems incorporating those machines. 6.685 explores concepts in electromechanics, using electric machinery as examples. It teaches an understanding of principles and analysis of electromechanical systems. By the end of the course, students are capable of doing electromechanical design of the major classes of rotating and linear electric machines, and have an understanding of the principles of the energy conversion parts of Mechatronics. In addition to design, students learn how to estimate the dynamic parameters of electric machines and understand what the implications of those parameters are on the performance of systems incorporating those machines.

Subjects

electric | electric | machine | machine | transformers | transformers | electromechanical | electromechanical | transducers | transducers | rotating | rotating | linear electric machines | linear electric machines | lumped parameter | lumped parameter | dc | dc | induction | induction | synchronous | synchronous | energy conversion | energy conversion | electromechanics | electromechanics | Mechatronics | Mechatronics | Electromechanical transducers | Electromechanical transducers | rotating electric machines | rotating electric machines | lumped-parameter elecromechanics | lumped-parameter elecromechanics | interaction electromechanics | interaction electromechanics | device characteristics | device characteristics | energy conversion density | energy conversion density | efficiency | efficiency | system interaction characteristics | system interaction characteristics | regulation | regulation | stability | stability | controllability | controllability | response | response | electric machines | electric machines | drive systems | drive systems | electric machinery | electric machinery | electromechanical systems | electromechanical systems | design | design | dynamic parameters | dynamic parameters | phenomena | phenomena | interactions | interactions | classical mechanics | classical mechanics

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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6.061 Introduction to Electric Power Systems (MIT) 6.061 Introduction to Electric Power Systems (MIT)

Description

This course is offered both to undergraduates (6.061) and graduates (6.979), where the graduate version has different problem sets and an additional term project. 6.061 / 6.979 is an introductory course in the field of electric power systems and electrical to mechanical energy conversion. Material encountered in the subject includes: Fundamentals of energy-handling electric circuits and electromechanical apparatus. Modeling of magnetic field devices and description of their behavior using appropriate models. Simplification of problems using transformation techniques. Power electric circuits, magnetic circuits, lumped parameter electromechanics, elements of linear and rotating electric machinery. Modeling of synchronous, induction and dc machinery. The course uses examples from current rese This course is offered both to undergraduates (6.061) and graduates (6.979), where the graduate version has different problem sets and an additional term project. 6.061 / 6.979 is an introductory course in the field of electric power systems and electrical to mechanical energy conversion. Material encountered in the subject includes: Fundamentals of energy-handling electric circuits and electromechanical apparatus. Modeling of magnetic field devices and description of their behavior using appropriate models. Simplification of problems using transformation techniques. Power electric circuits, magnetic circuits, lumped parameter electromechanics, elements of linear and rotating electric machinery. Modeling of synchronous, induction and dc machinery. The course uses examples from current rese

Subjects

electric power | electric power | electric power system | electric power system | electric circuits | electric circuits | electromechanical apparatus | electromechanical apparatus | magnetic field devices | magnetic field devices | transformation techniques | transformation techniques | magnetic circuits | magnetic circuits | lumped parameter electromechanics | lumped parameter electromechanics | linear electric machinery | linear electric machinery | rotating electric machinery | rotating electric machinery | synchronous machinery | synchronous machinery | induction machinery | induction machinery | dc machinery. | dc machinery. | mechanical energy conversion | mechanical energy conversion | energy | energy | new applications | new applications | dc machinery | dc machinery

License

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Readme file for Computer Science Concepts

Description

This readme file contains details of links to all the Computer Science Concepts module's material held on Jorum and information about the module as well.

Subjects

ukoer | strings lecture | induction and recursion lecture | induction lecture | recursion lecture | complexity lecture | languages lecture | computer sciences concepts test | computer science concepts test | computer science concepts assignment | computer science concepts practical | introduction | computer science concepts | computer science concept | computer science | strings and languages | strings and language | string and languages | string and language | string | language | languages | finite automata | automata | finite | push down automata | push down | prolog | data structures and algorithms | data structure and algorithms | data structures and algorithm | data structure and algorithm | data structures | data structure | algorithms | algorithm | revision exercises | revision | induction and recursion | induction | recursion | turing machines | turing machine | turing | machine | machines | complexity | grammar | grammar and languages | grammar and language | introduction lecture | computer science concepts lecture | computer science concept lecture | computer science lecture | strings and languages lecture | strings and language lecture | string and languages lecture | string and language lecture | string lecture | language lecture | finite automata lecture | automata lecture | finite lecture | push down automata lecture | push down lecture | prolog lecture | data structures and algorithms lecture | data structure and algorithms lecture | data structures and algorithm lecture | data structure and algorithm lecture | data structures lecture | data structure lecture | algorithms lecture | algorithm lecture | revision exercises lecture | revision lecture | turing machines lecture | turing machine lecture | turing lecture | machine lecture | machines lecture | computer science class test | computer science concept class test | computer science concepts class test | strings and languages class test | strings and language class test | string and languages class test | string and language class test | string class test | language class test | languages class test | introduction class test | grammar lecture | grammar and languages lecture | grammar and language lecture | computer science assignment | computer science concept assignment | strings and languages assignment | strings and language assignment | string and languages assignment | string and language assignment | string assignment | language assignment | languages assignment | finite automata class test | automata class test | finite class test | finite automata assignment | automata assignment | finite assignment | push down automata class test | push down class test | push down automata assignment | push down assignment | prolog class test | data structures and algorithms class test | data structure and algorithms class test | data structures and algorithm class test | data structure and algorithm class test | data structures class test | data structure class test | algorithms class test | algorithm class test | computer science practical | computer science concept practical | data structures and algorithms practical | data structure and algorithms practical | data structures and algorithm practical | data structure and algorithm practical | data structures practical | data structure practical | algorithms practical | algorithm practical | revision exercises class test | revision class test | induction and recursion class test | induction class test | recursion class test | induction and recursion assignment | induction assignment | recursion assignment | turing machines class test | turing machine class test | turing class test | machine class test | machines class test | turing machines assignment | turing machine assignment | turing assignment | machine assignment | machines assignment | complexity class test | grammar class test | grammar and languages class test | grammar and language class test | 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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6.867 Machine Learning (MIT) 6.867 Machine Learning (MIT)

Description

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered. 6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Subjects

machine learning algorithms | machine learning algorithms | statistical inference | statistical inference | representation | representation | generalization | generalization | model selection | model selection | linear/additive models | linear/additive models | active learning | active learning | boosting | boosting | support vector machines | support vector machines | hidden Markov models | hidden Markov models | Bayesian networks | Bayesian networks | classification | classification | linear regression | linear regression | modern machine learning methods | modern machine learning methods

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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6.867 Machine Learning (MIT) 6.867 Machine Learning (MIT)

Description

6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.  6.867 is an introductory course on machine learning which provides an overview of many techniques and algorithms in machine learning, beginning with topics such as simple perceptrons and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course gives the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered. 

Subjects

machine learning | machine learning | perceptrons | perceptrons | boosting | boosting | support vector machines | support vector machines | Markov | Markov | hidden Markov models | hidden Markov models | HMM | HMM | Bayesian networks | Bayesian networks | statistical inference | statistical inference | regression | regression | clustering | clustering | bias | bias | variance | variance | regularization | regularization | Generalized Linear Models | Generalized Linear Models | neural networks | neural networks | Support Vector Machine | Support Vector Machine | SVM | SVM | mixture models | mixture models | kernel density estimation | kernel density estimation | gradient descent | gradient descent | quadratic programming | quadratic programming | EM algorithm | EM algorithm | orward-backward algorithm | orward-backward algorithm | junction tree algorithm | junction tree algorithm | Gibbs sampling | Gibbs sampling

License

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

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2.670 Mechanical Engineering Tools (MIT) 2.670 Mechanical Engineering Tools (MIT)

Description

This course introduces the fundamentals of machine tool and computer tool use. Students work with a variety of machine tools including the bandsaw, milling machine, and lathe. Instruction given on MATLAB®, MAPLE®, XESS™, and CAD. Emphasis is on problem solving, not programming or algorithmic development. Assignments are project-oriented relating to mechanical engineering topics. It is recommended that students take this subject in the first IAP after declaring the major in Mechanical Engineering. This course was co-created by Prof. Douglas Hart and Dr. Kevin Otto. This course introduces the fundamentals of machine tool and computer tool use. Students work with a variety of machine tools including the bandsaw, milling machine, and lathe. Instruction given on MATLAB®, MAPLE®, XESS™, and CAD. Emphasis is on problem solving, not programming or algorithmic development. Assignments are project-oriented relating to mechanical engineering topics. It is recommended that students take this subject in the first IAP after declaring the major in Mechanical Engineering. This course was co-created by Prof. Douglas Hart and Dr. Kevin Otto.

Subjects

fundamentals of machine tool and computer tool use | fundamentals of machine tool and computer tool use | bandsaw | bandsaw | milling machine | milling machine | lathe | lathe | MATLAB | MATLAB | MAPLE | MAPLE | XESS | XESS | CAD | CAD | problem solving | problem solving | project-oriented | project-oriented | machine tool use | machine tool use | computer tool use | computer tool use | mechanical engineering projects | mechanical engineering projects | Inter Activities Period | Inter Activities Period | IAP | IAP | engine design | engine design | engine construction | engine construction | Stirling engines | Stirling engines

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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6.685 Electric Machines (MIT) 6.685 Electric Machines (MIT)

Description

6.685 explores concepts in electromechanics, using electric machinery as examples. It teaches an understanding of principles and analysis of electromechanical systems. By the end of the course, students are capable of doing electromechanical design of the major classes of rotating and linear electric machines and have an understanding of the principles of the energy conversion parts of Mechatronics. In addition to design, students learn how to estimate the dynamic parameters of electric machines and understand what the implications of those parameters are on the performance of systems incorporating those machines. 6.685 explores concepts in electromechanics, using electric machinery as examples. It teaches an understanding of principles and analysis of electromechanical systems. By the end of the course, students are capable of doing electromechanical design of the major classes of rotating and linear electric machines and have an understanding of the principles of the energy conversion parts of Mechatronics. In addition to design, students learn how to estimate the dynamic parameters of electric machines and understand what the implications of those parameters are on the performance of systems incorporating those machines.

Subjects

linear electric machines | linear electric machines | synchronous | synchronous | transformer | transformer | electromechanics | electromechanics | dc | dc | machines | machines | electromechanical transducer | electromechanical transducer | rotatingelectric | rotatingelectric | mechatronics | mechatronics | induction | induction | energy conversion | energy conversion | lumped parameter | lumped parameter | electric | electric | rotating | rotating | electromechanical | electromechanical | transducers | transducers

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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6.061 Introduction to Electric Power Systems (MIT) 6.061 Introduction to Electric Power Systems (MIT)

Description

This course is an introductory subject in the field of electric power systems and electrical to mechanical energy conversion. Electric power has become increasingly important as a way of transmitting and transforming energy in industrial, military and transportation uses. Examples of new uses for electric power include all manners of electric transportation systems (electric trains that run under catenary, diesel-electric railroad locomotion, 'maglev' medium and high speed tracked vehicles, electric transmission systems for ships, replacement of hydraulics in high performance actuators, aircraft launch and recovery systems, battery powered factory material transport systems, electric and hybrid electric cars and buses, even the 'more electric' airplane). The material in this subject w This course is an introductory subject in the field of electric power systems and electrical to mechanical energy conversion. Electric power has become increasingly important as a way of transmitting and transforming energy in industrial, military and transportation uses. Examples of new uses for electric power include all manners of electric transportation systems (electric trains that run under catenary, diesel-electric railroad locomotion, 'maglev' medium and high speed tracked vehicles, electric transmission systems for ships, replacement of hydraulics in high performance actuators, aircraft launch and recovery systems, battery powered factory material transport systems, electric and hybrid electric cars and buses, even the 'more electric' airplane). The material in this subject w

Subjects

electric power | electric power | electric power system | electric power system | electric circuits | electric circuits | electromechanical apparatus | electromechanical apparatus | magnetic field devices | magnetic field devices | transformation techniques | transformation techniques | magnetic circuits | magnetic circuits | lumped parameter electromechanics | lumped parameter electromechanics | linear electric machinery | linear electric machinery | rotating electric machinery | rotating electric machinery | synchronous machinery | synchronous machinery | induction machinery | induction machinery | dc machinery. | dc machinery. | mechanical energy conversion | mechanical energy conversion | energy | energy | new applications | new applications

License

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

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2.72 Elements of Mechanical Design (MIT) 2.72 Elements of Mechanical Design (MIT)

Description

This course provides an advanced treatment of machine elements such as bearings, springs, gears, cams, and mechanisms. Analysis of these elements includes extensive application of core engineering curriculum including solid mechanics and fluid dynamics. The course offers practice in skills needed for machine design such as estimation, drawing, and experimentation. Students work in small teams to design and build machines that address real-world challenges. This course provides an advanced treatment of machine elements such as bearings, springs, gears, cams, and mechanisms. Analysis of these elements includes extensive application of core engineering curriculum including solid mechanics and fluid dynamics. The course offers practice in skills needed for machine design such as estimation, drawing, and experimentation. Students work in small teams to design and build machines that address real-world challenges.

Subjects

machine design | machine design | hardware | hardware | project | project | machine element | machine element | design process | design process | design layout | design layout | prototype | prototype | mechanism | mechanism | engineering | engineering | fabrication | fabrication

License

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

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2.72 Elements of Mechanical Design (MIT) 2.72 Elements of Mechanical Design (MIT)

Description

In 2.72, students will learn the theory and experience the practice of machine design in the context of real world machine design hardware projects. Emphasis will be placed on the relationship of machine elements to the design process; including their availability, their uses, and the methods for determining their potential performance. Each group will complete and document a design layout for a prototype device. In 2.72, students will learn the theory and experience the practice of machine design in the context of real world machine design hardware projects. Emphasis will be placed on the relationship of machine elements to the design process; including their availability, their uses, and the methods for determining their potential performance. Each group will complete and document a design layout for a prototype device.

Subjects

machine design | machine design | hardware projects | hardware projects | machine elements | machine elements | design process | design process | design layout | design layout | prototype | prototype

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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6.685 Electric Machines (MIT)

Description

6.685 explores concepts in electromechanics, using electric machinery as examples. It teaches an understanding of principles and analysis of electromechanical systems. By the end of the course, students are capable of doing electromechanical design of the major classes of rotating and linear electric machines, and have an understanding of the principles of the energy conversion parts of Mechatronics. In addition to design, students learn how to estimate the dynamic parameters of electric machines and understand what the implications of those parameters are on the performance of systems incorporating those machines.

Subjects

electric | machine | transformers | electromechanical | transducers | rotating | linear electric machines | lumped parameter | dc | induction | synchronous | energy conversion | electromechanics | Mechatronics | Electromechanical transducers | rotating electric machines | lumped-parameter elecromechanics | interaction electromechanics | device characteristics | energy conversion density | efficiency | system interaction characteristics | regulation | stability | controllability | response | electric machines | drive systems | electric machinery | electromechanical systems | design | dynamic parameters | phenomena | interactions | classical mechanics

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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Machine Theory Machine Theory

Description

The aim of this course is to pre-dimension a machine depending on the requirements and requests that will be submitted. Analysis of the kinematic and dynamic of machines and spatial mechanisms. Analysis of the behavior of rotation and / or translation elements. Modeling and simulation of machines (modeling methods and computer simulation). The aim of this course is to pre-dimension a machine depending on the requirements and requests that will be submitted. Analysis of the kinematic and dynamic of machines and spatial mechanisms. Analysis of the behavior of rotation and / or translation elements. Modeling and simulation of machines (modeling methods and computer simulation).

Subjects

Hyperbolic | Hyperbolic | Ingenieria Mecanica | Ingenieria Mecanica | Bevel gears | Bevel gears | Synthesis of mechanisms | Synthesis of mechanisms | Kinematics | Kinematics | Spur gears | Spur gears | Spatial Mechanisms | Spatial Mechanisms | Pro-Engineer | Pro-Engineer | Mechanisms | Mechanisms | Gear trains | Gear trains | Rolling Bearings selection | Rolling Bearings selection | Balancing | Balancing | 2012 | 2012 | Simulation | Simulation | Cams design | Cams design | Plain bearings design | Plain bearings design | Analytical mechanics applied to machinery | Analytical mechanics applied to machinery | a Mecnica | a Mecnica | Helical | Helical | Flywheels | Flywheels | Friction | Friction | Lubrication | Lubrication | Software simulation | Software simulation

License

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

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18.465 Topics in Statistics: Statistical Learning Theory (MIT) 18.465 Topics in Statistics: Statistical Learning Theory (MIT)

Description

The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory. The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.

Subjects

machine learning algorithms | machine learning algorithms | boosting | boosting | support | support | support vector machines | support vector machines | neural networks | neural networks | Vapnik- Chervonenkis theory | Vapnik- Chervonenkis theory | concentration inequalities in product spaces | concentration inequalities in product spaces | empirical process theory | empirical process theory

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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4.112 Architecture Design Fundamentals I: Nano-Machines (MIT) 4.112 Architecture Design Fundamentals I: Nano-Machines (MIT)

Description

Includes audio/video content: AV special element video. This is the second undergraduate architecture design studio, which introduces design logic and skills that enable design thinking, representation, and development. Through the lens of nano-scale machines, technologies, and phenomena, students are asked to explore techniques for describing form, space, and architecture. Exercises encourage various connotations of the "machine" and challenge students to translate conceptual strategies into more integrated design propositions through both digital and analog means. Includes audio/video content: AV special element video. This is the second undergraduate architecture design studio, which introduces design logic and skills that enable design thinking, representation, and development. Through the lens of nano-scale machines, technologies, and phenomena, students are asked to explore techniques for describing form, space, and architecture. Exercises encourage various connotations of the "machine" and challenge students to translate conceptual strategies into more integrated design propositions through both digital and analog means.

Subjects

architecture | architecture | architectural design | architectural design | nano-machine | nano-machine | programmable matter | programmable matter | drawing | drawing | scripting | scripting | casting | casting | modeling | modeling | self-assembly | self-assembly | self-replication | self-replication | Processing | Processing | generation | generation | machine | machine | space | space | scale | scale | void | void | bounding box | bounding box | system | system | habitation | habitation | architectural space | architectural space

License

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

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2.72 Elements of Mechanical Design (MIT) 2.72 Elements of Mechanical Design (MIT)

Description

This is an advanced course on modeling, design, integration and best practices for use of machine elements such as bearings, springs, gears, cams and mechanisms. Modeling and analysis of these elements is based upon extensive application of physics, mathematics and core mechanical engineering principles (solid mechanics, fluid mechanics, manufacturing, estimation, computer simulation, etc.). These principles are reinforced via (1) hands-on laboratory experiences wherein students conduct experiments and disassemble machines and (2) a substantial design project wherein students model, design, fabricate and characterize a mechanical system that is relevant to a real world application. Students master the materials via problems sets that are directly related to, and coordinated with, the deliv This is an advanced course on modeling, design, integration and best practices for use of machine elements such as bearings, springs, gears, cams and mechanisms. Modeling and analysis of these elements is based upon extensive application of physics, mathematics and core mechanical engineering principles (solid mechanics, fluid mechanics, manufacturing, estimation, computer simulation, etc.). These principles are reinforced via (1) hands-on laboratory experiences wherein students conduct experiments and disassemble machines and (2) a substantial design project wherein students model, design, fabricate and characterize a mechanical system that is relevant to a real world application. Students master the materials via problems sets that are directly related to, and coordinated with, the deliv

Subjects

biology | biology | chemistry | chemistry | synthetic biology | synthetic biology | project | project | biotech | biotech | genetic engineering | genetic engineering | GMO | GMO | ethics | ethics | biomedical ethics | biomedical ethics | genetics | genetics | recombinant DNA | recombinant DNA | DNA | DNA | gene sequencing | gene sequencing | gene synthesis | gene synthesis | biohacking | biohacking | computational biology | computational biology | iGEM | iGEM | BioBrick | BioBrick | systems biology | systems biology | machine design | machine design | hardware | hardware | machine element | machine element | design process | design process | design layout | design layout | prototype | prototype | mechanism | mechanism | engineering | engineering | fabrication | fabrication | lathe | lathe | precision engineering | precision engineering | group project | group project | project management | project management | CAD | CAD | fatigue | fatigue | Gantt chart | Gantt chart

License

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

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18.465 Topics in Statistics: Statistical Learning Theory (MIT) 18.465 Topics in Statistics: Statistical Learning Theory (MIT)

Description

The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory. The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empirical process theory.

Subjects

machine learning algorithms | machine learning algorithms | boosting | boosting | support | support | support vector machines | support vector machines | neural networks | neural networks | Vapnik- Chervonenkis theory | Vapnik- Chervonenkis theory | concentration inequalities in product spaces | concentration inequalities in product spaces | empirical process theory | empirical process theory

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.965 Special Topics in Media Technology: Cooperative Machines (MIT) MAS.965 Special Topics in Media Technology: Cooperative Machines (MIT)

Description

This course examines the issues, principles, and challenges toward building machines that cooperate with humans and with other machines. Philosophical, scientific, and theoretical insights into this subject will be covered, as well as how these ideas are manifest in both natural and artificial systems (e.g. software agents and robots). This course examines the issues, principles, and challenges toward building machines that cooperate with humans and with other machines. Philosophical, scientific, and theoretical insights into this subject will be covered, as well as how these ideas are manifest in both natural and artificial systems (e.g. software agents and robots).

Subjects

cooperative machines | cooperative machines | robotics | robotics | electrical engineering | electrical engineering | manufacture | manufacture | human interaction | human interaction | perception | perception | emotion | emotion | theory of mind | theory of mind | behavior and the mind | behavior and the mind | robots | robots | human-machine collaboration | human-machine collaboration | intention and action | intention and action | teamwork | teamwork

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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Computer Science Concepts - Turing Machines

Description

This lecture forms part of the "Turing Machines" topic of the Computer Science Concepts module.

Subjects

ukoer | turing machines | computer science | computer science concept | computer science concepts | turing machine | turing | machine | machines | turing machines lecture | computer science lecture | computer science concept lecture | computer science concepts lecture | turing machine lecture | turing lecture | machine lecture | machines 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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Computer Science Concepts - Turing Machines

Description

This class test forms part of the "Turing Machines" topic of the Computer Science Concepts module.

Subjects

ukoer | computer science concepts test | turing machines | computer science | computer science concept | computer science concepts | turing machine | turing | machine | machines | turing machines class test | computer science class test | computer science concept class test | computer science concepts class test | turing machine class test | turing class test | machine class test | machines class test | 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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Computer Science Concepts - Turing Machines

Description

This class test forms part of the "Turing Machines" topic of the Computer Science Concepts module.

Subjects

ukoer | computer science concepts test | turing machines | computer science | computer science concept | computer science concepts | turing machine | turing | machine | machines | turing machines class test | computer science class test | computer science concept class test | computer science concepts class test | turing machine class test | turing class test | machine class test | machines class test | 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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Computer Science Concepts - Turing Machines

Description

This class test forms part of the "Turing Machines" topic of the Computer Science Concepts module.

Subjects

ukoer | computer science concepts test | turing machines | computer science | computer science concept | computer science concepts | turing machine | turing | machine | machines | turing machines class test | computer science class test | computer science concept class test | computer science concepts class test | turing machine class test | turing class test | machine class test | machines class test | 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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Computer Science Concepts - Turing Machines

Description

This assignment forms part of the "Turing Machines" topic of the Computer Science Concepts module.

Subjects

ukoer | turing machines | computer science | computer science concept | computer science concepts | turing machine | turing | machine | machines | turing machines assignment | computer science assignment | computer science concept assignment | computer science concepts assignment | turing machine assignment | turing assignment | machine assignment | machines assignment | 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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6.061 Introduction to Electric Power Systems (MIT)

Description

This course is offered both to undergraduates (6.061) and graduates (6.979), where the graduate version has different problem sets and an additional term project. 6.061 / 6.979 is an introductory course in the field of electric power systems and electrical to mechanical energy conversion. Material encountered in the subject includes: Fundamentals of energy-handling electric circuits and electromechanical apparatus. Modeling of magnetic field devices and description of their behavior using appropriate models. Simplification of problems using transformation techniques. Power electric circuits, magnetic circuits, lumped parameter electromechanics, elements of linear and rotating electric machinery. Modeling of synchronous, induction and dc machinery. The course uses examples from current rese

Subjects

electric power | electric power system | electric circuits | electromechanical apparatus | magnetic field devices | transformation techniques | magnetic circuits | lumped parameter electromechanics | linear electric machinery | rotating electric machinery | synchronous machinery | induction machinery | dc machinery. | mechanical energy conversion | energy | new applications | dc machinery

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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6.867 Machine Learning (MIT)

Description

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Subjects

machine learning algorithms | statistical inference | representation | generalization | model selection | linear/additive models | active learning | boosting | support vector machines | hidden Markov models | Bayesian networks | classification | linear regression | modern machine learning methods

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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6.867 Machine Learning (MIT)

Description

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, and Bayesian networks. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Subjects

machine learning algorithms | statistical inference | representation | generalization | model selection | linear/additive models | active learning | boosting | support vector machines | hidden Markov models | Bayesian networks | classification | linear regression | modern machine learning methods

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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