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Description

This course introduces fundamentals of shared and distributed memory programming, teaches you how to code using openMP and MPI respectively, and provides hands-on experience of parallel computing geared towards numerical applications. This course introduces fundamentals of shared and distributed memory programming, teaches you how to code using openMP and MPI respectively, and provides hands-on experience of parallel computing geared towards numerical applications.Subjects

OpenMP 3.0 | OpenMP 3.0 | MPI | MPI | Shared memory Programming | Shared memory Programming | Hybrid Programming | Hybrid Programming | MPI Runtime | MPI Runtime | Parallel Programming | Parallel Programming | data scoping | data scopingLicense

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.htmSite sourced from

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See all metadataProgramming in Java Language Programming in Java Language

Description

The course on Programming provides an introduction to basic programming techniques and paradigms. Students will learn the foundamentals of structured, procedural and object-oriented programming in the Java programming language. The course on Programming provides an introduction to basic programming techniques and paradigms. Students will learn the foundamentals of structured, procedural and object-oriented programming in the Java programming language.Subjects

Data and operators | Data and operators | Lenguajes y Sistemas Informaticos | Lenguajes y Sistemas Informaticos | Java | Java | Components of a program | Components of a program | Programming | Programming | Introduction to classes and objects | Introduction to classes and objects | Algorithms with arrays | Algorithms with arrays | Utility classes | Utility classes | Control flow statements | Control flow statements | ía Informática | ía Informática | 2011 | 2011 | Methods | MethodsLicense

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See all metadataProgramming in C Language Programming in C Language

Description

The course on C Programming provides an introduction to the most common programming techniques and paradigms. Students will learn the fundamentals of imperative structured programming in the C programming language. The course on C Programming provides an introduction to the most common programming techniques and paradigms. Students will learn the fundamentals of imperative structured programming in the C programming language.Subjects

Sort | Sort | Search | Search | Lenguajes y Sistemas Informaticos | Lenguajes y Sistemas Informaticos | C Language | C Language | Programming | Programming | Functions | Functions | Computer Science | Computer Science | C. Computacion e Inteligencia Artificial | C. Computacion e Inteligencia Artificial | ía en Tecnologías Industriales | ía en Tecnologías Industriales | Complex data types | Complex data types | 2013 | 2013 | Control Structures | Control StructuresLicense

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See all metadata8A: Logic Programming, Part 1 8A: Logic Programming, Part 1

Description

Topics covered: Logic Programming, Part 1 Instructors: Hal Abelson and Gerald Jay Sussman Subtitles for this course are provided through the generous assistance of Henry Baker, Hoofar Pourzand, Heather Wood, Aleksejs Truhans, Steven Edwards, George Menhorn, and Mahendra Kumar.Transcript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - stream: YouTube (CC BY-NC-SA) Topics covered: Logic Programming, Part 1 Instructors: Hal Abelson and Gerald Jay Sussman Subtitles for this course are provided through the generous assistance of Henry Baker, Hoofar Pourzand, Heather Wood, Aleksejs Truhans, Steven Edwards, George Menhorn, and Mahendra Kumar.Transcript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - stream: YouTube (CC BY-NC-SA)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.htm8B: Logic Programming, Part 2 8B: Logic Programming, Part 2

Description

Topics covered: Logic Programming, Part 2 Instructors: Hal Abelson and Gerald Jay Sussman Subtitles for this course are provided through the generous assistance of Henry Baker, Hoofar Pourzand, Heather Wood, Aleksejs Truhans, Steven Edwards, George Menhorn, and Mahendra Kumar.Transcript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - stream: YouTube (CC BY-NC-SA) Topics covered: Logic Programming, Part 2 Instructors: Hal Abelson and Gerald Jay Sussman Subtitles for this course are provided through the generous assistance of Henry Baker, Hoofar Pourzand, Heather Wood, Aleksejs Truhans, Steven Edwards, George Menhorn, and Mahendra Kumar.Transcript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - stream: YouTube (CC BY-NC-SA)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.htmDescription

Topics covered: Dynamic Programming, Longest Common Subsequence Instructors: Prof. Erik Demaine, Prof. Charles LeisersonTranscript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - download: Internet Archive (MP3)Video - download: iTunes U (MP4)Video - download: iTunes U (MP3)Video - stream: VideoLectures.net Video - stream: YouTube (CC BY-NC-SA) Topics covered: Dynamic Programming, Longest Common Subsequence Instructors: Prof. Erik Demaine, Prof. Charles LeisersonTranscript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - download: Internet Archive (MP3)Video - download: iTunes U (MP4)Video - download: iTunes U (MP3)Video - stream: VideoLectures.net Video - stream: YouTube (CC BY-NC-SA)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.htmDescription

Topics covered: Shortest Paths II: Bellman-Ford, Linear Programming, Difference ConstraintsInstructors: Prof. Erik Demaine, Prof. Charles LeisersonTranscript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - download: Internet Archive (MP3)Video - download: iTunes U (MP4)Video - download: iTunes U (MP3)Video - stream: VideoLectures.net Video - stream: YouTube (CC BY-NC-SA) Topics covered: Shortest Paths II: Bellman-Ford, Linear Programming, Difference ConstraintsInstructors: Prof. Erik Demaine, Prof. Charles LeisersonTranscript: PDFSubtitles: SRTThumbnail - JPG (OCW)Video - download: Internet Archive (MP4)Video - download: Internet Archive (MP3)Video - download: iTunes U (MP4)Video - download: iTunes U (MP3)Video - stream: VideoLectures.net Video - stream: YouTube (CC BY-NC-SA)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.htm20.180 Biological Engineering Programming (MIT) 20.180 Biological Engineering Programming (MIT)

Description

In this course problems from biological engineering are used to develop structured computer programming skills and explore the theory and practice of complex systems design and construction. The official course Web site can be viewed at: BE.180 Biological Engineering Programming. In this course problems from biological engineering are used to develop structured computer programming skills and explore the theory and practice of complex systems design and construction. The official course Web site can be viewed at: BE.180 Biological Engineering Programming.Subjects

biological engineering problems | biological engineering problems | structured computer programming skills | structured computer programming skills | theory and practice of complex systems design | theory and practice of complex systems design | theory and design of complex systems construction | theory and design of complex systems constructionLicense

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.htmSite sourced from

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See all metadata12.950 Parallel Programming for Multicore Machines Using OpenMP and MPI (MIT)

Description

This course introduces fundamentals of shared and distributed memory programming, teaches you how to code using openMP and MPI respectively, and provides hands-on experience of parallel computing geared towards numerical applications.Subjects

OpenMP 3.0 | MPI | Shared memory Programming | Hybrid Programming | MPI Runtime | Parallel Programming | data scopingLicense

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.htmSite sourced from

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See all metadataIntroduction to Neuro Linguistic Programming - Learning Package

Description

A learning activity about Neuro Linguistic Programming which provides: a brief introduction to Neuro-Linguistic Programming, an approach which is popular in business and personal development. You will find out where NLP came from; what it actually means and how it can help you to communicate better and reach your potential.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/Site sourced from

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See all metadataIntroduction to Neuro Linguistic Programming - Raw Materials

Description

The raw materials which make up the Introduction to Neuro Linguistic Programming package which provides a brief introduction to Neuro-Linguistic Programming, an approach which is popular in business and personal development. You will find out where NLP came from; what it actually means and how it can help you to communicate better and reach your potential.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/Site sourced from

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See all metadataModelling with Neuro Linguistic Programming - Raw Materials

Description

The raw materials for the Modelling with Neuro Linguistic Programming Learning Activity which is about modelling positive behaviours using Neuro Linguistic Programming. It introduces the concepts of modelling and NLP, explores what they are and how they function in order to demonstrate their importance in effective communication.Subjects

communication | beliefs | modelling | behaviours | employability | nlp | neuro linguistic programming | ukoer | administrative studies | N000License

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/Site sourced from

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See all metadata6.0001 Introduction to Computer Science and Programming in Python (MIT)

Description

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

Computation | Branching | Iteration | Strings | Guess and check | Approximations | Bisection | Decomposition | Abstractions | Functions | Tuples | Lists | Aliasing | Mutability | Recursion | Dictionaries | Testing | Debugging | Exceptions | Assertions | Object Oriented Programming | Python Classes | Inheritance | Program Efficiency | Searching | SortingLicense

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.htmSite sourced from

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This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations. This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages (finite and infinite horizon). We will also discuss some approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.Subjects

dynamic programming | dynamic programming | stochastic control | stochastic control | decision making | decision making | uncertainty | uncertainty | sequential decision making | sequential decision making | finite horizon | finite horizon | infinite horizon | infinite horizon | approximation methods | approximation methods | state space | state space | large state space | large state space | optimal control | optimal control | dynamical system | dynamical system | dynamic programming and optimal control | dynamic programming and optimal control | deterministic systems | deterministic systems | shortest path | shortest path | state information | state information | rollout | rollout | stochastic shortest path | stochastic shortest path | approximate dynamic programming | approximate dynamic programmingLicense

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.htmSite sourced from

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This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.Subjects

programming languages | programming languages | techniques used by physical scientists | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++ | C++ | Matlab | Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification | methods of dissemination and verificationLicense

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.htmSite sourced from

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This 6-unit P/D/F course will provide a gentle introduction to programming using Python for highly motivated students with little or no prior experience in programming computers over the first two weeks of IAP. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. Lectures will be interactive, featuring in-class exercises with lots of support from the course staff. This class is designed to help prepare students for 6.01 Introduction to EECS I. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs This 6-unit P/D/F course will provide a gentle introduction to programming using Python for highly motivated students with little or no prior experience in programming computers over the first two weeks of IAP. The course will focus on planning and organizing programs, as well as the grammar of the Python programming language. Lectures will be interactive, featuring in-class exercises with lots of support from the course staff. This class is designed to help prepare students for 6.01 Introduction to EECS I. 6.01 assumes some knowledge of Python upon entering; the course material for 6.189 has been specially designed to make sure that concepts important to 6.01 are covered. This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runsSubjects

python | python | programming | programming | introduction to programming | introduction to programming | programming for beginners | programming for beginners | variables | variables | operators | operators | control flow | control flow | functions | functions | strings | strings | lists | lists | environment diagrams | environment diagrams | list comprehensions | list comprehensions | hangman | hangman | dictionaries | dictionaries | graphics | graphics | python graphics | python graphics | objects | objects | oop | oop | inheritance | inheritance | tetris | tetris | tetris game | tetris gameLicense

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.htmSite sourced from

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The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization. The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization.Subjects

theory | theory | algorithms | algorithms | integer optimization | integer optimization | formulations and relaxations | formulations and relaxations | algebra and geometry of integer optimization | algebra and geometry of integer optimization | algorithms for integer optimization | algorithms for integer optimization | extensions of integer optimization | extensions of integer optimization | 15.083 | 15.083License

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.htmSite sourced from

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This course offers an introduction to optimization problems, algorithms, and their complexity, emphasizing basic methodologies and the underlying mathematical structures. The main topics covered include: Theory and algorithms for linear programming Network flow problems and algorithms Introduction to integer programming and combinatorial problems This course offers an introduction to optimization problems, algorithms, and their complexity, emphasizing basic methodologies and the underlying mathematical structures. The main topics covered include: Theory and algorithms for linear programming Network flow problems and algorithms Introduction to integer programming and combinatorial problemsSubjects

optimization | optimization | algorithms | algorithms | linear programming | linear programming | network flow problems | network flow problems | integer programming | integer programming | combinatorial problems | combinatorial problems | mathematics | mathematics | mathematical programming | mathematical programming | 6.251 | 6.251 | 15.081 | 15.081License

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.htmSite sourced from

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This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.Subjects

programming languages | programming languages | techniques used by physical scientists | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++ | C++ | Matlab | Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification | methods of dissemination and verificationLicense

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.htmSite sourced from

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This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python™ programming language. This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python™ programming language.Subjects

computer science | computer science | computation | computation | problem solving | problem solving | Python programming | Python programming | recursion | recursion | binary search | binary search | classes | classes | inheritance | inheritance | libraries | libraries | algorithms | algorithms | optimization problems | optimization problems | modules | modules | simulation | simulation | big O notation | big O notation | control flow | control flow | exceptions | exceptions | building computational models | building computational models | software engineering | software engineeringLicense

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.htmSite sourced from

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See all metadata6.854J Advanced Algorithms (MIT) 6.854J Advanced Algorithms (MIT)

Description

This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science. This is a graduate course on the design and analysis of algorithms, covering several advanced topics not studied in typical introductory courses on algorithms. It is especially designed for doctoral students interested in theoretical computer science.Subjects

Linear Programming | Linear Programming | Network Flows | Network Flows | Approximation Algorithms | Approximation Algorithms | Planarity Testing of Graphs | Planarity Testing of Graphs | Number-Theoretic Algorithms | Number-Theoretic Algorithms | Data Structures | Data Structures | 6.854 | 6.854 | 18.415 | 18.415License

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.htmSite sourced from

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This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). Approximation methods for problems involving large state spaces are also presented and discussed. This course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). Approximation methods for problems involving large state spaces are also presented and discussed.Subjects

dynamic programming | dynamic programming | | stochastic control | | stochastic control | | mathematics | optimization | | | mathematics | optimization | | algorithms | | algorithms | | probability | | probability | | Markov chains | | Markov chains | | optimal control | optimal control | stochastic control | stochastic control | mathematics | mathematics | optimization | optimization | algorithms | algorithms | probability | probability | Markov chains | Markov chainsLicense

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.htmSite sourced from

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This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.Subjects

programming languages | programming languages | techniques used by physical scientists | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++; Matlab | C++; Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification. | methods of dissemination and verification. | C++ | C++ | Matlab | Matlab | programming languages | techniques used by physical scientists | programming languages | techniques used by physical scientistsLicense

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.htmSite sourced from

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The basic objective of Unified Engineering is to give a solid understanding of the fundamental disciplines of aerospace engineering, as well as their interrelationships and applications. These disciplines are Materials and Structures (M); Computers and Programming (C); Fluid Mechanics (F); Thermodynamics (T); Propulsion (P); and Signals and Systems (S). In choosing to teach these subjects in a unified manner, the instructors seek to explain the common intellectual threads in these disciplines, as well as their combined application to solve engineering Systems Problems (SP). Throughout the year, the instructors emphasize the connections among the disciplines.Technical RequirementsMicrosoft® Excel software is recommended for viewing the .xls files The basic objective of Unified Engineering is to give a solid understanding of the fundamental disciplines of aerospace engineering, as well as their interrelationships and applications. These disciplines are Materials and Structures (M); Computers and Programming (C); Fluid Mechanics (F); Thermodynamics (T); Propulsion (P); and Signals and Systems (S). In choosing to teach these subjects in a unified manner, the instructors seek to explain the common intellectual threads in these disciplines, as well as their combined application to solve engineering Systems Problems (SP). Throughout the year, the instructors emphasize the connections among the disciplines.Technical RequirementsMicrosoft® Excel software is recommended for viewing the .xls filesSubjects

Unified | Unified | Unified Engineering | Unified Engineering | aerospace | aerospace | CDIO | CDIO | C-D-I-O | C-D-I-O | conceive | conceive | design | design | implement | implement | operate | operate | team | team | team-based | team-based | discipline | discipline | materials | materials | structures | structures | materials and structures | materials and structures | computers | computers | programming | programming | computers and programming | computers and programming | fluids | fluids | fluid mechanics | fluid mechanics | thermodynamics | thermodynamics | propulsion | propulsion | signals | signals | systems | systems | signals and systems | signals and systems | systems problems | systems problems | fundamentals | fundamentals | technical communication | technical communication | graphical communication | graphical communication | communication | communication | reading | reading | research | research | experimentation | experimentation | personal response system | personal response system | prs | prs | active learning | active learning | First law | First law | first law of thermodynamics | first law of thermodynamics | thermo-mechanical | thermo-mechanical | energy | energy | energy conversion | energy conversion | aerospace power systems | aerospace power systems | propulsion systems | propulsion systems | aerospace propulsion systems | aerospace propulsion systems | heat | heat | work | work | thermal efficiency | thermal efficiency | forms of energy | forms of energy | energy exchange | energy exchange | processes | processes | heat engines | heat engines | engines | engines | steady-flow energy equation | steady-flow energy equation | energy flow | energy flow | flows | flows | path-dependence | path-dependence | path-independence | path-independence | reversibility | reversibility | irreversibility | irreversibility | state | state | thermodynamic state | thermodynamic state | performance | performance | ideal cycle | ideal cycle | simple heat engine | simple heat engine | cycles | cycles | thermal pressures | thermal pressures | temperatures | temperatures | linear static networks | linear static networks | loop method | loop method | node method | node method | linear dynamic networks | linear dynamic networks | classical methods | classical methods | state methods | state methods | state concepts | state concepts | dynamic systems | dynamic systems | resistive circuits | resistive circuits | sources | sources | voltages | voltages | currents | currents | Thevinin | Thevinin | Norton | Norton | initial value problems | initial value problems | RLC networks | RLC networks | characteristic values | characteristic values | characteristic vectors | characteristic vectors | transfer function | transfer function | ada | ada | ada programming | ada programming | programming language | programming language | software systems | software systems | programming style | programming style | computer architecture | computer architecture | program language evolution | program language evolution | classification | classification | numerical computation | numerical computation | number representation systems | number representation systems | assembly | assembly | SimpleSIM | SimpleSIM | RISC | RISC | CISC | CISC | operating systems | operating systems | single user | single user | multitasking | multitasking | multiprocessing | multiprocessing | domain-specific classification | domain-specific classification | recursive | recursive | execution time | execution time | fluid dynamics | fluid dynamics | physical properties of a fluid | physical properties of a fluid | fluid flow | fluid flow | mach | mach | reynolds | reynolds | conservation | conservation | conservation principles | conservation principles | conservation of mass | conservation of mass | conservation of momentum | conservation of momentum | conservation of energy | conservation of energy | continuity | continuity | inviscid | inviscid | steady flow | steady flow | simple bodies | simple bodies | airfoils | airfoils | wings | wings | channels | channels | aerodynamics | aerodynamics | forces | forces | moments | moments | equilibrium | equilibrium | freebody diagram | freebody diagram | free-body | free-body | free body | free body | planar force systems | planar force systems | equipollent systems | equipollent systems | equipollence | equipollence | support reactions | support reactions | reactions | reactions | static determinance | static determinance | determinate systems | determinate systems | truss analysis | truss analysis | trusses | trusses | method of joints | method of joints | method of sections | method of sections | statically indeterminate | statically indeterminate | three great principles | three great principles | 3 great principles | 3 great principles | indicial notation | indicial notation | rotation of coordinates | rotation of coordinates | coordinate rotation | coordinate rotation | stress | stress | extensional stress | extensional stress | shear stress | shear stress | notation | notation | plane stress | plane stress | stress equilbrium | stress equilbrium | stress transformation | stress transformation | mohr | mohr | mohr's circle | mohr's circle | principal stress | principal stress | principal stresses | principal stresses | extreme shear stress | extreme shear stress | strain | strain | extensional strain | extensional strain | shear strain | shear strain | strain-displacement | strain-displacement | compatibility | compatibility | strain transformation | strain transformation | transformation of strain | transformation of strain | mohr's circle for strain | mohr's circle for strain | principal strain | principal strain | extreme shear strain | extreme shear strain | uniaxial stress-strain | uniaxial stress-strain | material properties | material properties | classes of materials | classes of materials | bulk material properties | bulk material properties | origin of elastic properties | origin of elastic properties | structures of materials | structures of materials | atomic bonding | atomic bonding | packing of atoms | packing of atoms | atomic packing | atomic packing | crystals | crystals | crystal structures | crystal structures | polymers | polymers | estimate of moduli | estimate of moduli | moduli | moduli | composites | composites | composite materials | composite materials | modulus limited design | modulus limited design | material selection | material selection | materials selection | materials selection | measurement of elastic properties | measurement of elastic properties | stress-strain | stress-strain | stress-strain relations | stress-strain relations | anisotropy | anisotropy | orthotropy | orthotropy | measurements | measurements | engineering notation | engineering notation | Hooke | Hooke | Hooke's law | Hooke's law | general hooke's law | general hooke's law | equations of elasticity | equations of elasticity | boundary conditions | boundary conditions | multi-disciplinary | multi-disciplinary | models | models | engineering systems | engineering systems | experiments | experiments | investigations | investigations | experimental error | experimental error | design evaluation | design evaluation | evaluation | evaluation | trade studies | trade studies | effects of engineering | effects of engineering | social context | social context | engineering drawings | engineering drawings | 16.01 | 16.01 | 16.02 | 16.02 | 16.03 | 16.03 | 16.04 | 16.04License

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.htmSite sourced from

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This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. Students first learn the basic usage of each language, common types of problems encountered, and techniques for solving a variety of problems encountered in contemporary research: examination of data with visualization techniques, numerical analysis, and methods of dissemination and verification. No prior programming experience is required.Technical RequirementsAny number of development tools can be used to compile and run the .c and .f files found on this course site. C++ compiler is required to This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. Students first learn the basic usage of each language, common types of problems encountered, and techniques for solving a variety of problems encountered in contemporary research: examination of data with visualization techniques, numerical analysis, and methods of dissemination and verification. No prior programming experience is required.Technical RequirementsAny number of development tools can be used to compile and run the .c and .f files found on this course site. C++ compiler is required toSubjects

programming languages | techniques used by physical scientists | programming languages | techniques used by physical scientists | FORTRAN | FORTRAN | C | C | C++ | C++ | Matlab | Matlab | Mathematica | Mathematica | program design | program design | algorithm development and verification | algorithm development and verification | comparative advantages and disadvantages of different languages | comparative advantages and disadvantages of different languages | examination of data with visualization techniques | examination of data with visualization techniques | numerical analysis | numerical analysis | methods of dissemination and verification | methods of dissemination and verification | algorithms | algorithms | formula | formula | formulae | formulae | computer programs | computer programs | graphics | graphics | computing languages | computing languages | structure | structure | documentation | documentation | program interface | program interface | syntax | syntax | advanced modeling | advanced modeling | simulation systems | simulation systemsLicense

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.htmSite sourced from

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Click to get HTML | Click to get attribution | Click to get URLAll metadata

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