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6.01 Introduction to Electrical Engineering and Computer Science I (MIT) 6.01 Introduction to Electrical Engineering and Computer Science I (MIT)

Description

6.01 explores fundamental ideas in electrical engineering and computer science, in the context of working with mobile robots. Key engineering principles, such as abstraction and modularity, are applied in the design of computer programs, electronic circuits, discrete-time controllers, and noisy and/or uncertain systems. 6.01 explores fundamental ideas in electrical engineering and computer science, in the context of working with mobile robots. Key engineering principles, such as abstraction and modularity, are applied in the design of computer programs, electronic circuits, discrete-time controllers, and noisy and/or uncertain systems.

Subjects

robots | robots | python | python | computer programs | computer programs | circuits | circuits | systems | systems | inheritance | inheritance | recursion | recursion | functional programming | functional programming | signals | signals | control | control | circuit abstractions | circuit abstractions | probability | probability | discrete probability | discrete probability | search algorithms | search algorithms | state machines | state machines | probabilistic state estimation | probabilistic state estimation | decision-making | decision-making | search | search | python robots | python robots

License

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6.189 A Gentle Introduction to Programming Using Python (MIT) 6.189 A Gentle Introduction to Programming Using Python (MIT)

Description

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 runs

Subjects

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 game

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

Description

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems. This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

Subjects

algorithms | algorithms | python | python | python cost model | python cost model | binary search trees | binary search trees | hashing | hashing | sorting | sorting | searching | searching | shortest paths | shortest paths | dynamic programming | dynamic programming | numerics | numerics | document distance | document distance | longest common substring | longest common substring | dijkstra | dijkstra | fibonacci | fibonacci | image resizing | image resizing | chaining | chaining | hash functions | hash functions | priority queues | priority queues | breadth first search | breadth first search | depth first search | depth first search | memoization | memoization | divide and conquer | divide and conquer

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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6.047 Computational Biology: Genomes, Networks, Evolution (MIT) 6.047 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution

Subjects

computational biology | computational biology | algorithms | algorithms | machine learning | machine learning | biology | biology | biological datasets | biological datasets | genomics | genomics | proteomics | proteomics | genomes | genomes | sequence analysis | sequence analysis | sequence alignment | sequence alignment | genome assembly | genome assembly | network motifs | network motifs | network evolution | network evolution | graph algorithms | graph algorithms | phylogenetics | phylogenetics | comparative genomics | comparative genomics | python | python | probability | probability | statistics | statistics | entropy | entropy | information | information

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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DemOnline

Description

Authors:  Andy Buffler, Michael Malahe The website catalogues both lecture demonstrations and VPython scripts. Clicked 131 times. Last clicked 01/27/2015 - 06:13. Teaching & Learning Context:  <p>DemOnline is a collection of demonstrations and VPython scripts for use in an introductory physics course.</p>

Subjects

Physics | Science | Downloadable Documents | Text/HTML Webpages | Video | Training Materials | English | Post-secondary | demonstrations | electromagnetism | light and waves | mechanics | physics | python | thermodynamics and matter | vpython

License

http://creativecommons.org/licenses/by/2.5/za/

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PHYLAB2 - Course 2 laboratory

Description

Authors:  Roger Fearick The lab focuses on an introduction to computational methods, in support of the Classical and Quantum Mechanics lectures.   Clicked 146 times. Last clicked 01/27/2015 - 06:15. Teaching & Learning Context:  <p>This is a second year laboratory course that accompanies PHY2014F and PHY2015S</p>

Subjects

Physics | Science | Downloadable Documents | Text/HTML Webpages | Lecture Notes | English | Post-secondary | introduction to uncertainties | physics | python | uncertainties | vpython

License

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6.047 Computational Biology (MIT) 6.047 Computational Biology (MIT)

Description

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets. This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Subjects

Genomes | Genomes | Networks | Networks | Evolution | Evolution | computational biology | computational biology | genomics | genomics | comparative genomics | comparative genomics | epigenomics | epigenomics | functional genomics | motifs | functional genomics | motifs | phylogenomics | phylogenomics | personal genomics | personal genomics | algorithms | algorithms | machine learning | machine learning | biology | biology | biological datasets | biological datasets | proteomics | proteomics | sequence analysis | sequence analysis | sequence alignment | sequence alignment | genome assembly | genome assembly | network motifs | network motifs | network evolution | network evolution | graph algorithms | graph algorithms | phylogenetics | phylogenetics | python | python | probability | probability | statistics | statistics | entropy | entropy | information | information

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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6.01 Introduction to Electrical Engineering and Computer Science I (MIT)

Description

6.01 explores fundamental ideas in electrical engineering and computer science, in the context of working with mobile robots. Key engineering principles, such as abstraction and modularity, are applied in the design of computer programs, electronic circuits, discrete-time controllers, and noisy and/or uncertain systems.

Subjects

robots | python | computer programs | circuits | systems | inheritance | recursion | functional programming | signals | control | circuit abstractions | probability | discrete probability | search algorithms | state machines | probabilistic state estimation | decision-making | search | python robots

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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6.189 A Gentle Introduction to Programming Using Python (MIT)

Description

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

Subjects

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

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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6.006 Introduction to Algorithms (MIT)

Description

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

Subjects

algorithms | python | python cost model | binary search trees | hashing | sorting | searching | shortest paths | dynamic programming | numerics | document distance | longest common substring | dijkstra | fibonacci | image resizing | chaining | hash functions | priority queues | breadth first search | depth first search | memoization | divide and conquer

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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Introduction to Python Programming, Part 1

Description

Authors:  Michelle Kuttel This set of six files comprises the slides from first 6 weeks of our 12 week first year course on Python Programming. It is an introduction aimed at students who have not programmed before. Clicked 787 times. Last clicked 11/10/2014 - 20:54. Teaching & Learning Context:  A first half of a first course on how to program in Python.

Subjects

Computer Science | Science | Downloadable Documents | Lecture Notes | English | Primary | computer programming | programming | python

License

http://creativecommons.org/licenses/by-sa/2.5/za/

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Interactions

Description

Authors:  Andy Buffler, Roger Fearick, Indresan Govender, Andre Peshier MODERN MECHANICS: Matter and interactions, conservation laws, the momentum principle, atomic nature of matter, conservation of energy, energy in macroscopic systems, energy quantization, multiparti Clicked 594 times. Last clicked 10/08/2014 - 18:58. Teaching & Learning Context:  <p>PHY1004W is a first-year, calculus-based introductory Physics course for Science students intending to continue with second-year Physics.</p>

Subjects

Physics | Science | Downloadable Documents | Text/HTML Webpages | Lecture Notes | English | Post-secondary | atoms | electricity | energy | magnetism | mechanics | physics | thermal physics | vpython

License

http://creativecommons.org/licenses/by/2.5/za/

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PHY2009S - Intermediate Physics (2nd year)

Description

Authors:  Andy Buffler VECTOR FIELDS IN PHYSICS: Vector calculus; div, grad, curl; line-, surface- and volume integrals; Gauss' Theorem; Stokes' Theorem; simple applications to fluid dynamics and electromagnetism STATIS Clicked 331 times. Last clicked 09/18/2014 - 17:13. Teaching & Learning Context:  <p>PHY2009S is a physics course primarily for students who have not completed PHY1004W, to prepare them for PHY2014F and PHY2015S.</p>

Subjects

Physics | Science | Downloadable Documents | Text/HTML Webpages | Lecture Notes | English | Post-secondary | energy | entropy | gas laws | vpython

License

http://creativecommons.org/licenses/by/2.5/za/

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Python for physics

Description

Authors:  Roger Fearick Python is a programming language which is increasingly being used for computation in physics. Why is this? * Python is easy to learn. Clicked 906 times. Last clicked 01/27/2015 - 21:00. Teaching & Learning Context:  <p>This is an introduction to the Python programming language for computational physics.</p>

Subjects

Physics | Science | Downloadable Documents | Lecture Notes | English | Post-secondary | computational physics | physics | programming | python

License

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Snake handler

Description

This image comes from a collection of glass slides of fairground scenes found in the stores at Discovery Museum, Newcastle upon Tyne. www.flickr.com/photos/twm_news/sets/72157627692102509/ We have no information about the photographer or where the photographs were taken. We welcome any new information you are able to share. (Copyright) We're happy for you to share these digital images within the spirit of The Commons. Please cite 'Tyne & Wear Archives & Museums' when reusing. Certain restrictions on high quality reproductions and commercial use of the original physical version apply though; if you're unsure please email archives@twmuseums.org.uk

Subjects

circus | newcastle | fairground | blackandwhite | carnival | python | boaconstrictor | cart | snakecharmer | trousers | portrait | performer | documentaryphotography | behindthescenes | archivephotograph | circusanimal | entertainer | menageries | glassslide | fairgroundscene | showbusiness | pattern | wheels | stagecoach | carriage | timber | metal | dress | grass | snake | man | discoverymuseum | newcastleupontyne | pegs | rope | blur | intriguing | cleanshaven | smooth | costume | cord | rail | handle | culture | surreal | unusual

License

No known copyright restrictions

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6.047 Computational Biology (MIT)

Description

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Subjects

Genomes | Networks | Evolution | computational biology | genomics | comparative genomics | epigenomics | functional genomics | motifs | phylogenomics | personal genomics | algorithms | machine learning | biology | biological datasets | proteomics | sequence analysis | sequence alignment | genome assembly | network motifs | network evolution | graph algorithms | phylogenetics | python | probability | statistics | entropy | information

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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6.881 Computational Personal Genomics: Making Sense of Complete Genomes (MIT)

Description

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

Subjects

Genomes | Networks | Evolution | computational biology | genomics | comparative genomics | epigenomics | functional genomics | motifs | phylogenomics | personal genomics | algorithms | machine learning | biology | biological datasets | proteomics | sequence analysis | sequence alignment | genome assembly | network motifs | network evolution | graph algorithms | phylogenetics | python | probability | statistics | entropy | information

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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6.0002 Introduction to Computational Thinking and Data Science (MIT)

Description

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

Subjects

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

License

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

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21M.385 Interactive Music Systems (MIT)

Description

This course explores audio synthesis, musical structure, human computer interaction (HCI), and visual presentation for the creation of interactive musical experiences. Topics include audio synthesis; mixing and looping; MIDI sequencing; generative composition; motion sensors; music games; and graphics for UI, visualization, and aesthetics. Weekly programming assignments in python are included. Student teams build an original, dynamic, and engaging interactive music system for their final project.

Subjects

audio synthesis | musical structure | human computer interaction | HCI | visual presentation | interactive musical experiences | mixing | looping | MIDI sequencing | generative composition; motion sensors | music games | graphics | UI | visualization | aesthetics | python

License

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

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