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Readme file for Introduction to Artificial Intelligence

Description

This readme file contains details of links to all the Introduction to Artificial Intelligence module's material held on Jorum and information about the module as well.

Subjects

ukoer | evolutionary algorithm lecture | algorithm tutorial | genetic algorithm lecture | genetic algorithm example | evolutionary computation tutorial | artificial intelligence lecture | artificial intelligence tutorial | random processes reading material | semantic web reading material | neural networks video | evolutionary computation test | artificial intelligence test | knowledge representation test | neural networks test | evolutionary algorithm | genetic computation | genetic programming | evolutionary computation | artificial intelligence | introduction to artificial intelligence | search | problem solving | revision | knowledge representation | semantic web | neural network | neural networks | artificial neural networks | swarm intelligence | collective intelligence | robot societies | genetic computation lecture | genetic programming lecture | evolutionary computation lecture | introduction to artificial intelligence lecture | evolutionary algorithm tutorial | genetic computation tutorial | genetic programming tutorial | introduction to artificial intelligence tutorial | evolutionary algorithm example | genetic computation example | genetic programming example | evolutionary computation example | artificial intelligence example | introduction to artificial intelligence example | search lecture | problem solving lecture | search tutorial | problem solving tutorial | search example | problem solving example | revision reading material | search reading material | artificial intelligence reading material | introduction to artificial intelligence reading material | revision lecture | knowledge representation lecture | semantic web lecture | knowledge representation practical | semantic web practical | artificial intelligence practical | introduction to artificial intelligence practical | knowledge representation reading material | knowledge representation notes | semantic web notes | artificial intelligence notes | introduction to artificial intelligence notes | neural network lecture | neural networks lecture | artificial neural networks lecture | neural network reading material | neural networks reading material | artificial neural networks reading material | neural network practical | neural networks practical | artificial neural networks practical | neural network viewing material | neural networks viewing material | artificial neural networks viewing material | artificial intelligence viewing material | introduction to artificial intelligence viewing material | swarm intelligence lecture | collective intelligence lecture | robot societies lecture | swarm intelligence tutorial | collective intelligence tutorial | robot societies tutorial | evolutionary algorithm test | genetic computation test | genetic programming test | introduction to artificial intelligence test | search test | problem solving test | semantic web test | neural network test | artificial neural networks test | g700 | ai | g700 lecture | ai lecture | g700 tutorial | ai tutorial | g700 example | ai example | g700 reading material | ai reading material | g700 practical | ai practical | g700 notes | ai notes | g700 viewing material | ai viewing material | g700 test | ai 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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MAS.110 Fundamentals of Computational Media Design (MIT) MAS.110 Fundamentals of Computational Media Design (MIT)

Description

This class introduces principles of analysis and synthesis in the computational medium. Expressive examples that illustrate the intersection of computation with the traditional arts are developed on a weekly basis. Hands-on design exercises are continually framed and examined in the larger context of contemporary digital art. This class introduces principles of analysis and synthesis in the computational medium. Expressive examples that illustrate the intersection of computation with the traditional arts are developed on a weekly basis. Hands-on design exercises are continually framed and examined in the larger context of contemporary digital art.

Subjects

analysis | analysis | synthesis | synthesis | computational media | computational media | computational and traditional arts | computational and traditional arts | design | design | programming | programming | javascript | javascript | contemporary digital art | contemporary digital art | machine age | machine age | media design | media design | analog vs digital art | analog vs digital art | graphic design | graphic design | web design | web design | photography | photography | storytelling | storytelling | modern art | modern art | computation | computation | arts | arts | design exercises | design exercises | studio | studio | analog art | analog art

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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18.404J Theory of Computation (MIT) 18.404J Theory of Computation (MIT)

Description

A more extensive and theoretical treatment of the material in 18.400J, Automata, Computability, and Complexity, emphasizing computability and computational complexity theory. Regular and context-free languages. Decidable and undecidable problems, reducibility, recursive function theory. Time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems. A more extensive and theoretical treatment of the material in 18.400J, Automata, Computability, and Complexity, emphasizing computability and computational complexity theory. Regular and context-free languages. Decidable and undecidable problems, reducibility, recursive function theory. Time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems.

Subjects

computability | computability | computational complexity theory | computational complexity theory | Regular and context-free languages | Regular and context-free languages | Decidable and undecidable problems | Decidable and undecidable problems | reducibility | reducibility | recursive function theory | recursive function theory | Time and space measures on computation | Time and space measures on computation | completeness | completeness | hierarchy theorems | hierarchy theorems | inherently complex problems | inherently complex problems | oracles | oracles | probabilistic computation | probabilistic computation | interactive proof systems | interactive proof systems | 18.404 | 18.404 | 6.840 | 6.840

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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6.856J Randomized Algorithms (MIT) 6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms. This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.

Subjects

Randomized Algorithms | Randomized Algorithms | algorithms | algorithms | efficient in time and space | efficient in time and space | randomization | randomization | computational problems | computational problems | data structures | data structures | graph algorithms | graph algorithms | optimization | optimization | geometry | geometry | Markov chains | Markov chains | sampling | sampling | estimation | estimation | geometric algorithms | geometric algorithms | parallel and distributed algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and ditributed algorithm | parallel and distributed algorithm | parallel and distributed algorithm | random sampling | random sampling | random selection of witnesses | random selection of witnesses | symmetry breaking | symmetry breaking | randomized computational models | randomized computational models | hash tables | hash tables | skip lists | skip lists | minimum spanning trees | minimum spanning trees | shortest paths | shortest paths | minimum cuts | minimum cuts | convex hulls | convex hulls | linear programming | linear programming | fixed dimension | fixed dimension | arbitrary dimension | arbitrary dimension | approximate counting | approximate counting | parallel algorithms | parallel algorithms | online algorithms | online algorithms | derandomization techniques | derandomization techniques | probabilistic analysis | probabilistic analysis | computational number theory | computational number theory | simplicity | simplicity | speed | speed | design | design | basic probability theory | basic probability theory | application | application | randomized complexity classes | randomized complexity classes | game-theoretic techniques | game-theoretic techniques | Chebyshev | Chebyshev | moment inequalities | moment inequalities | limited independence | limited independence | coupon collection | coupon collection | occupancy problems | occupancy problems | tail inequalities | tail inequalities | Chernoff bound | Chernoff bound | conditional expectation | conditional expectation | probabilistic method | probabilistic method | random walks | random walks | algebraic techniques | algebraic techniques | probability amplification | probability amplification | sorting | sorting | searching | searching | combinatorial optimization | combinatorial optimization | approximation | approximation | counting problems | counting problems | distributed algorithms | distributed algorithms | 6.856 | 6.856 | 18.416 | 18.416

License

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18.404J Theory of Computation (MIT) 18.404J Theory of Computation (MIT)

Description

This graduate level course is more extensive and theoretical treatment of the material in Computability, and Complexity (6.045J / 18.400J). Topics include Automata and Language Theory, Computability Theory, and Complexity Theory. This graduate level course is more extensive and theoretical treatment of the material in Computability, and Complexity (6.045J / 18.400J). Topics include Automata and Language Theory, Computability Theory, and Complexity Theory.

Subjects

Computability | computational complexity theory | Computability | computational complexity theory | Regular and context-free languages | Regular and context-free languages | Decidable and undecidable problems | reducibility | recursive function theory | Decidable and undecidable problems | reducibility | recursive function theory | Time and space measures on computation | completeness | hierarchy theorems | inherently complex problems | oracles | probabilistic computation | and interactive proof systems | Time and space measures on computation | completeness | hierarchy theorems | inherently complex problems | oracles | probabilistic computation | and interactive proof systems

License

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6.172 Performance Engineering of Software Systems (MIT) 6.172 Performance Engineering of Software Systems (MIT)

Description

Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard pr Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard pr

Subjects

performance engineering | performance engineering | parallelism | parallelism | computational power | computational power | complexity | complexity | computation | computation | efficiency | efficiency | high performance | high performance | software system | software system | performance analysis | performance analysis | algorithms | algorithms | instruction level optimization | instruction level optimization | cache | cache | memory | memory | parallel programming | parallel programming | distributed systems | distributed systems | algorithmic design | algorithmic design | profile | profile | multithreaded | multithreaded | cilk | cilk | cilk arts | cilk arts | ray tracer | ray tracer | render | render

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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6.045J Automata, Computability, and Complexity (MIT) 6.045J Automata, Computability, and Complexity (MIT)

Description

This course is offered to undergraduates and introduces basic mathematical models of computation and the finite representation of infinite objects. The course is slower paced than 6.840J/18.404J. Topics covered include: finite automata and regular languages, context-free languages, Turing machines, partial recursive functions, Church's Thesis, undecidability, reducibility and completeness, time complexity and NP-completeness, probabilistic computation, and interactive proof systems. This course is offered to undergraduates and introduces basic mathematical models of computation and the finite representation of infinite objects. The course is slower paced than 6.840J/18.404J. Topics covered include: finite automata and regular languages, context-free languages, Turing machines, partial recursive functions, Church's Thesis, undecidability, reducibility and completeness, time complexity and NP-completeness, probabilistic computation, and interactive proof systems.

Subjects

automata | automata | computability | computability | complexity | complexity | mathematical models | mathematical models | computation | computation | finite representation | finite representation | infinite objects | infinite objects | finite automata | finite automata | regular languages | regular languages | context-free languages | context-free languages | Turing machines | Turing machines | partial recursive functions | partial recursive functions | Church's Thesis | Church's Thesis | undecidability | undecidability | reducibility | reducibility | completeness | completeness | time complexity | time complexity | NP-completeness | NP-completeness | probabilistic computation | probabilistic computation | interactive proof systems | interactive proof systems | 6.045 | 6.045 | 18.400 | 18.400

License

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6.852J Distributed Algorithms (MIT) 6.852J Distributed Algorithms (MIT)

Description

This course intends to provide a rigorous introduction to the most important research results in the area of distributed algorithms, and prepare interested students to carry out independent research in distributed algorithms. Topics covered include: design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks, process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration is given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation are also discussed. Detailed information on the This course intends to provide a rigorous introduction to the most important research results in the area of distributed algorithms, and prepare interested students to carry out independent research in distributed algorithms. Topics covered include: design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks, process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration is given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation are also discussed. Detailed information on the

Subjects

distributed algorithms | distributed algorithms | algorithm | algorithm | concurrent algorithms | concurrent algorithms | distributed networks | distributed networks | process synchronization | process synchronization | computational resources | computational resources | distributed consensus | distributed consensus | distributed graph algorithms | distributed graph algorithms | distributed termination | distributed termination | deadlock detection | deadlock detection | concurrency control | concurrency control | communication | communication | clock synchronization | clock synchronization | fault tolerance | fault tolerance | distributed computation | distributed computation | 6.852 | 6.852 | 18.437 | 18.437

License

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6.004 Computation Structures (MIT) 6.004 Computation Structures (MIT)

Description

6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops a series of building blocks - logic gates, combinational and sequential circuits, finite-state machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples.6.004 is required material for any EECS undergraduate who wants to understand (and ultimately design) digital systems. A good grasp of the material is essential for later courses in digital design, computer architecture and systems. Before taking 6.004, students should feel comfortable using computers; a rudimentary knowledge of programming language concepts (6.001) and electrical fundamentals (6.002) is assumed. 6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops a series of building blocks - logic gates, combinational and sequential circuits, finite-state machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples.6.004 is required material for any EECS undergraduate who wants to understand (and ultimately design) digital systems. A good grasp of the material is essential for later courses in digital design, computer architecture and systems. Before taking 6.004, students should feel comfortable using computers; a rudimentary knowledge of programming language concepts (6.001) and electrical fundamentals (6.002) is assumed.

Subjects

computation | computation | computation structure | computation structure | primitives | primitives | gates | gates | nstructions | nstructions | procedures | procedures | processes | processes | concurrency | concurrency | instruction set design | instruction set design | software structure | software structure | digital system | digital system | MOS transistor | MOS transistor | logic gate | logic gate | combinational circuit | combinational circuit | sequential circuit | | sequential circuit | | finite-state machines | finite-state machines | sequential circuit | sequential circuit | computer architecture | computer architecture | programming | programming | RISC processor | RISC processor | instructions | instructions

License

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6.00 Introduction to Computer Science and Programming (MIT) 6.00 Introduction to Computer Science and Programming (MIT)

Description

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 engineering

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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4.500 Introduction to Design Computing (MIT) 4.500 Introduction to Design Computing (MIT)

Description

This course introduces students to architectural design and computation through the use of computer modeling, rendering, and digital fabrication. The focus is on the exploration of space and place-making through the use of computer rendering and design construction and fabrication. Students design a small building using computer models leading to a full package of physical and virtual materials, from computer generated drawings to rapid, prototyped models. This course introduces students to architectural design and computation through the use of computer modeling, rendering, and digital fabrication. The focus is on the exploration of space and place-making through the use of computer rendering and design construction and fabrication. Students design a small building using computer models leading to a full package of physical and virtual materials, from computer generated drawings to rapid, prototyped models.

Subjects

architectural design and computation | architectural design and computation | computer modeling | computer modeling | rendering | rendering | digital fabrication | digital fabrication | exploration of space | exploration of space | place making | place making | computer rendering | computer rendering | design construction | design construction | CAD CAM fabrication | CAD CAM fabrication | computer models | computer models | computer aided drawings | computer aided drawings | rapid prototyped models | rapid prototyped models | architecture | architecture | design | design | computation | computation

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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4.510 Digital Design Fabrication (MIT) 4.510 Digital Design Fabrication (MIT)

Description

This class serves as an introductory subject in advanced computing, rapid prototyping, and CAD/CAM fabrication for architects. It focuses on the relationship between design and various forms of computer modeling as input, and CAD/CAM tools as output material. It presents the process of design and construction using CAD files introduced by the office of Gehry Partners during the construction of the Guggenheim Museum in Bilbao, Spain. It is taught in phases starting with rapid prototyping and ending with digital mockups of building components fabricated from CAD files on a one-to-one scale. This class serves as an introductory subject in advanced computing, rapid prototyping, and CAD/CAM fabrication for architects. It focuses on the relationship between design and various forms of computer modeling as input, and CAD/CAM tools as output material. It presents the process of design and construction using CAD files introduced by the office of Gehry Partners during the construction of the Guggenheim Museum in Bilbao, Spain. It is taught in phases starting with rapid prototyping and ending with digital mockups of building components fabricated from CAD files on a one-to-one scale.

Subjects

architectural design and computation | architectural design and computation | computer modeling | computer modeling | rendering | rendering | digital fabrication | digital fabrication | exploration of space | exploration of space | place making | place making | computer rendering | computer rendering | design construction | design construction | CAD/CAM fabrication | CAD/CAM fabrication | computer models | computer models | computer aided drawings | computer aided drawings | rapid prototyped models | rapid prototyped models | architecture | architecture | design | design | computation | computation | CAD CAM fabrication | CAD CAM fabrication

License

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4.206 Introduction to Design Computing (MIT) 4.206 Introduction to Design Computing (MIT)

Description

This course will introduce students to architectural design and computation through the use of computer modeling, rendering and digital fabrication. The course focuses on teaching architectural design with CAD drawing, modeling, rendering and rapid prototyping. Students will be required to build computer models that will lead to a full package of architectural explorations within a computational environment. Each semester will explore a particular historical period in architecture and the work of a selected architect. This course will introduce students to architectural design and computation through the use of computer modeling, rendering and digital fabrication. The course focuses on teaching architectural design with CAD drawing, modeling, rendering and rapid prototyping. Students will be required to build computer models that will lead to a full package of architectural explorations within a computational environment. Each semester will explore a particular historical period in architecture and the work of a selected architect.

Subjects

architectural design and computation | architectural design and computation | computer modeling | computer modeling | rendering | rendering | digital fabrication | digital fabrication | exploration of space | exploration of space | place making | place making | computer rendering | computer rendering | design construction | design construction | CAD CAM fabrication | CAD CAM fabrication | computer models | computer models | computer aided drawings | computer aided drawings | rapid prototyped models | rapid prototyped models | architecture | architecture | design | design | computation | computation | representational mediums | representational mediums | architectural design | architectural design | complex phenomena | complex phenomena | constructs | constructs | information visualization | information visualization | programming | programming | computer graphics | computer graphics | data respresentation | data respresentation

License

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4.212 Design Fabrication (MIT) 4.212 Design Fabrication (MIT)

Description

Design Fabrication is an introductory course in the field of advanced computing, prototyping and building fabrication. The class is focused on the relationship between design, various forms of computer modeling both explicit and generative and the physical representation of information using rapid prototyping devices. Design Fabrication is an introductory course in the field of advanced computing, prototyping and building fabrication. The class is focused on the relationship between design, various forms of computer modeling both explicit and generative and the physical representation of information using rapid prototyping devices.

Subjects

architectural design and computation | architectural design and computation | computer modeling | computer modeling | rendering | rendering | digital fabrication | digital fabrication | exploration of space | exploration of space | place making | place making | computer rendering | computer rendering | design construction | design construction | CAD CAM fabrication | CAD CAM fabrication | computer models | computer models | computer aided drawings | computer aided drawings | rapid prototyped models | rapid prototyped models | architecture | architecture | design | design | computation | computation

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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6.050J Information and Entropy (MIT) 6.050J Information and Entropy (MIT)

Description

6.050J / 2.110J presents the unified theory of information with applications to computing, communications, thermodynamics, and other sciences. It covers digital signals and streams, codes, compression, noise, and probability, reversible and irreversible operations, information in biological systems, channel capacity, maximum-entropy formalism, thermodynamic equilibrium, temperature, the Second Law of Thermodynamics, and quantum computation. Designed for MIT freshmen as an elective, this course has been jointly developed by MIT's Departments of Electrical Engineering and Computer Science and Mechanical Engineering. There is no known course similar to 6.050J / 2.110J offered at any other university.  6.050J / 2.110J presents the unified theory of information with applications to computing, communications, thermodynamics, and other sciences. It covers digital signals and streams, codes, compression, noise, and probability, reversible and irreversible operations, information in biological systems, channel capacity, maximum-entropy formalism, thermodynamic equilibrium, temperature, the Second Law of Thermodynamics, and quantum computation. Designed for MIT freshmen as an elective, this course has been jointly developed by MIT's Departments of Electrical Engineering and Computer Science and Mechanical Engineering. There is no known course similar to 6.050J / 2.110J offered at any other university. 

Subjects

information and entropy | information and entropy | computing | computing | communications | communications | thermodynamics | thermodynamics | digital signals and streams | digital signals and streams | codes | codes | compression | compression | noise | noise | probability | probability | reversible operations | reversible operations | irreversible operations | irreversible operations | information in biological systems | information in biological systems | channel capacity | channel capacity | aximum-entropy formalism | aximum-entropy formalism | thermodynamic equilibrium | thermodynamic equilibrium | temperature | temperature | second law of thermodynamics quantum computation | second law of thermodynamics quantum computation | maximum-entropy formalism | maximum-entropy formalism | second law of thermodynamics | second law of thermodynamics | quantum computation | quantum computation | biological systems | biological systems | unified theory of information | unified theory of information | digital signals | digital signals | digital streams | digital streams | bits | bits | errors | errors | processes | processes | inference | inference | maximum entropy | maximum entropy | physical systems | physical systems | energy | energy | quantum information | quantum information | 6.050 | 6.050 | 2.110 | 2.110

License

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16.100 Aerodynamics (MIT) 16.100 Aerodynamics (MIT)

Description

This course extends fluid mechanic concepts from Unified Engineering to the aerodynamic performance of wings and bodies in sub/supersonic regimes. 16.100 generally has four components: subsonic potential flows, including source/vortex panel methods; viscous flows, including laminar and turbulent boundary layers; aerodynamics of airfoils and wings, including thin airfoil theory, lifting line theory, and panel method/interacting boundary layer methods; and supersonic and hypersonic airfoil theory. Course material varies each year depending upon the focus of the design problem. Technical RequirementsFile decompression software, such as Winzip® or StuffIt®, is required to open the .tar files found on this course site. MATLAB&#1 This course extends fluid mechanic concepts from Unified Engineering to the aerodynamic performance of wings and bodies in sub/supersonic regimes. 16.100 generally has four components: subsonic potential flows, including source/vortex panel methods; viscous flows, including laminar and turbulent boundary layers; aerodynamics of airfoils and wings, including thin airfoil theory, lifting line theory, and panel method/interacting boundary layer methods; and supersonic and hypersonic airfoil theory. Course material varies each year depending upon the focus of the design problem. Technical RequirementsFile decompression software, such as Winzip® or StuffIt®, is required to open the .tar files found on this course site. MATLAB&#1

Subjects

aerodynamics | aerodynamics | airflow | airflow | air | air | body | body | aircraft | aircraft | aerodynamic modes | aerodynamic modes | aero | aero | forces | forces | flow | flow | computational | computational | CFD | CFD | aerodynamic analysis | aerodynamic analysis | lift | lift | drag | drag | potential flows | potential flows | imcompressible | imcompressible | supersonic | supersonic | subsonic | subsonic | panel method | panel method | vortex lattice method | vortex lattice method | boudary layer | boudary layer | transition | transition | turbulence | turbulence | inviscid | inviscid | viscous | viscous | euler | euler | navier-stokes | navier-stokes | wind tunnel | wind tunnel | flow similarity | flow similarity | non-dimensional | non-dimensional | mach number | mach number | reynolds number | reynolds number | integral momentum | integral momentum | airfoil | airfoil | wing | wing | stall | stall | friction drag | friction drag | induced drag | induced drag | wave drag | wave drag | pressure drag | pressure drag | fluid element | fluid element | shear strain | shear strain | normal strain | normal strain | vorticity | vorticity | divergence | divergence | substantial derviative | substantial derviative | laminar | laminar | displacement thickness | displacement thickness | momentum thickness | momentum thickness | skin friction | skin friction | separation | separation | velocity profile | velocity profile | 2-d panel | 2-d panel | 3-d vortex | 3-d vortex | thin airfoil | thin airfoil | lifting line | lifting line | aspect ratio | aspect ratio | twist | twist | camber | camber | wing loading | wing loading | roll moments | roll moments | finite volume approximation | finite volume approximation | shocks | shocks | expansion fans | expansion fans | shock-expansion theory | shock-expansion theory | transonic | transonic | critical mach number | critical mach number | wing sweep | wing sweep | Kutta condition | Kutta condition | team project | team project | blended-wing-body | blended-wing-body | computational fluid dynamics | computational fluid dynamics | Incompressible | Incompressible

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6.045J Automata, Computability, and Complexity (MIT) 6.045J Automata, Computability, and Complexity (MIT)

Description

This course introduces basic mathematical models of computation and the finite representation of infinite objects. Topics covered include: finite automata and regular languages, context-free languages, Turing machines, partial recursive functions, Church's Thesis, undecidability, reducibility and completeness, time complexity and NP-completeness, probabilistic computation, and interactive proof systems. This course introduces basic mathematical models of computation and the finite representation of infinite objects. Topics covered include: finite automata and regular languages, context-free languages, Turing machines, partial recursive functions, Church's Thesis, undecidability, reducibility and completeness, time complexity and NP-completeness, probabilistic computation, and interactive proof systems.

Subjects

automata | automata | computability | computability | complexity | complexity | mathematical models | mathematical models | computation | computation | finite representation | finite representation | infinite objects | infinite objects | finite automata | finite automata | regular languages | regular languages | context-free languages | context-free languages | Turing machines | Turing machines | partial recursive functions | partial recursive functions | Church's Thesis | Church's Thesis | undecidability | undecidability | reducibility | reducibility | completeness | completeness | time complexity | time complexity | NP-completeness | NP-completeness | probabilistic computation | probabilistic computation | interactive proof systems | interactive proof systems | 6.045 | 6.045 | 18.400 | 18.400

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6.852J Distributed Algorithms (MIT) 6.852J Distributed Algorithms (MIT)

Description

6.852J / 18.437J intends to: (1) provide a rigorous introduction to the most important research results in the area of distributed algorithms, and (2) prepare interested students to carry out independent research in distributed algorithms. Topics covered include: design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks, process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration is given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation are also discussed. 6.852J / 18.437J intends to: (1) provide a rigorous introduction to the most important research results in the area of distributed algorithms, and (2) prepare interested students to carry out independent research in distributed algorithms. Topics covered include: design and analysis of concurrent algorithms, emphasizing those suitable for use in distributed networks, process synchronization, allocation of computational resources, distributed consensus, distributed graph algorithms, election of a leader in a network, distributed termination, deadlock detection, concurrency control, communication, and clock synchronization. Special consideration is given to issues of efficiency and fault tolerance. Formal models and proof methods for distributed computation are also discussed.

Subjects

distributed algorithms | distributed algorithms | algorithm | algorithm | concurrent algorithms | concurrent algorithms | distributed networks | distributed networks | process synchronization | process synchronization | computational resources | computational resources | distributed consensus | distributed consensus | distributed graph algorithms | distributed graph algorithms | distributed termination | distributed termination | deadlock detection | deadlock detection | concurrency control | concurrency control | communication | communication | clock synchronization | clock synchronization | fault tolerance | fault tolerance | distributed computation | distributed computation | 6.852 | 6.852 | 18.437 | 18.437

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9.641J Introduction to Neural Networks (MIT) 9.641J Introduction to Neural Networks (MIT)

Description

Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development. Organization of synaptic connectivity as the basis of neural computation and learning. Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Backpropagation and Hebbian learning. Models of perception, motor control, memory, and neural development.

Subjects

synaptic connectivity | synaptic connectivity | computation | computation | learning | learning | multilayer perceptrons | multilayer perceptrons | recurrent networks | recurrent networks | amplifiers | amplifiers | attractors | attractors | hybrid computation | hybrid computation | Backpropagation | Backpropagation | Hebbian learning | Hebbian learning | perception | perception | motor control | motor control | memory | memory | neural development | neural development | 9.641 | 9.641 | 8.594 | 8.594

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6.00 Introduction to Computer Science and Programming (MIT) 6.00 Introduction to Computer Science and Programming (MIT)

Description

Includes audio/video content: AV lectures. 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. Includes audio/video content: AV lectures. 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 engineering

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6.004 Computation Structures (MIT) 6.004 Computation Structures (MIT)

Description

6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops a series of building blocks — logic gates, combinational and sequential circuits, finite-state machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples. 6.004 is required material for any EECS undergraduate who wants to understand (and ultimately design) digital systems. A good grasp of the material is essential for later courses in digital design, computer architecture and systems. The problem sets and lab exercises are intended to give students "hands-on" experience in designing digital systems; each student completes a gate-level design for a reduced instruction set computer 6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops a series of building blocks — logic gates, combinational and sequential circuits, finite-state machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples. 6.004 is required material for any EECS undergraduate who wants to understand (and ultimately design) digital systems. A good grasp of the material is essential for later courses in digital design, computer architecture and systems. The problem sets and lab exercises are intended to give students "hands-on" experience in designing digital systems; each student completes a gate-level design for a reduced instruction set computer

Subjects

computation | computation | computation structure | computation structure | primitives | primitives | gates | gates | instructions | instructions | procedures | procedures | processes | processes | concurrency | concurrency | instruction set design | instruction set design | software structure | software structure | digital system | digital system | MOS transistor | MOS transistor | logic gate | logic gate | combinational circuit | combinational circuit | sequential circuit | sequential circuit | finite-state machines | finite-state machines | computer architecture | computer architecture | programming | programming | RISC processor | RISC processor

License

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6.877J Computational Evolutionary Biology (MIT) 6.877J Computational Evolutionary Biology (MIT)

Description

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data. Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

Subjects

6.877 | 6.877 | HST.949 | HST.949 | computational approaches | computational approaches | evolutionary biology | evolutionary biology | evolutionary theory and inferential logic of evolution by natural selection | evolutionary theory and inferential logic of evolution by natural selection | computational and algorithmic implications and requirements of evolutionary models | computational and algorithmic implications and requirements of evolutionary models | whole-genome species comparison | whole-genome species comparison | phylogenetic tree construction | phylogenetic tree construction | molecular evolution | molecular evolution | homology and development | homology and development | optimization and evolvability | optimization and evolvability | heritability | heritability | disease evolution | disease evolution | detecting selection in human populations | and evolution of language | detecting selection in human populations | and evolution of language | extensive laboratory exercises in model-building and analyzing evolutionary data | extensive laboratory exercises in model-building and analyzing evolutionary data

License

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6.876J Advanced Topics in Cryptography (MIT) 6.876J Advanced Topics in Cryptography (MIT)

Description

The topics covered in this course include interactive proofs, zero-knowledge proofs, zero-knowledge proofs of knowledge, non-interactive zero-knowledge proofs, secure protocols, two-party secure computation, multiparty secure computation, and chosen-ciphertext security. The topics covered in this course include interactive proofs, zero-knowledge proofs, zero-knowledge proofs of knowledge, non-interactive zero-knowledge proofs, secure protocols, two-party secure computation, multiparty secure computation, and chosen-ciphertext security.

Subjects

interactive proofs | interactive proofs | zero-knowledge proofs | zero-knowledge proofs | zero-knowledge proofs of knowledge | zero-knowledge proofs of knowledge | non-interactive zero-knowledge proofs | non-interactive zero-knowledge proofs | secure protocols | secure protocols | two-party secure computation | two-party secure computation | multiparty secure computation | multiparty secure computation | chosen-ciphertext security | chosen-ciphertext security | 6.876 | 6.876 | 18.426 | 18.426

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6.863J Natural Language and the Computer Representation of Knowledge (MIT) 6.863J Natural Language and the Computer Representation of Knowledge (MIT)

Description

6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design. 6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

Subjects

natural language processing | natural language processing | computational methods | computational methods | computer science | computer science | artificial intelligence | artificial intelligence | linguistic theory | linguistic theory | psycholinguistics | psycholinguistics | applications | applications | thematic structure | thematic structure | lexical-conceptual structure | lexical-conceptual structure | semantic structure | semantic structure | pragmatic structure | pragmatic structure | discourse structure | discourse structure | phonology | phonology | morphology | morphology | 2-level morphology | 2-level morphology | kimmo | kimmo | hmm tagging | hmm tagging | tagging | tagging | rule-based tagging | rule-based tagging | part of speech tagging | part of speech tagging | brill tagger | brill tagger | parsing | parsing | syntax | syntax | automata | automata | word modeling | word modeling | grammars | grammars | parsing algorithms | parsing algorithms | shift-reduce parsers | shift-reduce parsers | Earley's algorithm | Earley's algorithm | chart parsing | chart parsing | context-free parsing | context-free parsing | feature-based parsing | feature-based parsing | natural language system design | natural language system design | integrated lexicon | integrated lexicon | syntactic features | syntactic features | semantic interpretation | semantic interpretation | compositionality | compositionality | quantifiers | quantifiers | lexical semantic | lexical semantic | semantics | semantics | machine translation | machine translation | language learning | language learning | computational models of language | computational models of language | origins of language | origins of language | 6.863 | 6.863 | 9.611 | 9.611

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9.012 The Brain and Cognitive Sciences II (MIT) 9.012 The Brain and Cognitive Sciences II (MIT)

Description

This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives. This class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

Subjects

brain | brain | behavioral | behavioral | perception | perception | attention | attention | working memory | working memory | recognition | recognition | recall | recall | language | language | cognitive science | cognitive science | computation | computation | visual perception | visual perception | memory | memory | cognitive architecture | cognitive architecture | learning | learning | reasoning | reasoning | decision-making | decision-making | cognitive development | cognitive development | behavioral perspective | behavioral perspective | computational perspective | computational perspective | neural perspective | neural perspective

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