MIT OpenCourseWare: New Courses in Electrical Engineering and Computer ScienceNew courses in Electrical Engineering and Computer Science from MIT OpenCourseWare, provider of free and open MIT course materials.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science
2019-08-16T20:56:01+05:00MIT OpenCourseWare https://ocw.mit.eduen-USContent 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.htm6.004 Computation Structures (MIT)This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. It covers the topics including multilevel implementation strategies, definition of new primitives (e.g., gates, instructions, procedures, processes) and their mechanization using lower-level elements. It also includes analysis of potential concurrency, precedence constraints and performance measures, pipelined and multidimensional systems, instruction set design issues and architectural support for contemporary software structures.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-004-computation-structures-spring-2017
Spring2017Terman, Chris2019-07-12T18:32:34+05:006.004en-UScomputationcomputation structureprimitives, gatesinstructionsproceduresprocessesconcurrencyinstruction set designsoftware structuredigital systemMOS transistorlogic gatecombinational circuitsequential circuitfinite-state machinescomputer architectureprogrammingRISC processorMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.057 Introduction to MATLAB (MIT)This is an accelerated introduction to MATLAB® and its popular toolboxes. Lectures are interactive, with students conducting sample MATLAB problems in real time. The course includes problem-based MATLAB assignments. Students must provide their own laptop and software. This is great preparation for classes that use MATLAB.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-057-introduction-to-matlab-january-iap-2019
January IAP2019Celiker, Orhan2019-07-02T11:03:29+05:006.057en-USmatlabsimulinkvisualizationmathworksprogrammingcurve fittingfunctionsnumerical techniquesgraphingequationsplottingimage plotssurface plotsdebuggingsymbolic toolboximage processingMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.436J Fundamentals of Probability (MIT)This is a course on the fundamentals of probability geared towards first or second-year graduate students who are interested in a rigorous development of the subject. The course covers sample space, random variables, expectations, transforms, Bernoulli and Poisson processes, finite Markov chains, and limit theorems. There is also a number of additional topics such as: language, terminology, and key results from measure theory; interchange of limits and expectations; multivariate Gaussian distributions; and deeper understanding of conditional distributions and expectations.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-436j-fundamentals-of-probability-fall-2018
Fall2018Polyanskiy, Yury2019-05-07T16:44:13+05:006.436J15.085Jen-USprobabilistic modelssample spaceconditional probabilityrandom variablesabstract integrationproduct measurejoint distributionsconvolutionmultivariate normal distributionstochastic processesmarkov chainsmartingalesMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.011 Signals, Systems and Inference (MIT)This course covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-011-signals-systems-and-inference-spring-2018
Spring2018Verghese, GeorgeOppenheim, Alan V.Hagelstein, Peter2018-10-23T15:43:53+05:006.011en-USsignals and systemstransform representationstate-space modelsstate observersstate feedbackprobabilistic modelsrandom processespower spectral densityhypothesis testingsignal detectionMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.033 Computer System Engineering (MIT)This class covers topics on the engineering of computer software and hardware systems. Topics include techniques for controlling complexity; strong modularity using client-server design, operating systems; performance, networks; naming; security and privacy; fault-tolerant systems, atomicity and coordination of concurrent activities, and recovery; impact of computer systems on society.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-033-computer-system-engineering-spring-2018
Spring2018LaCurts, Katrina2018-10-05T13:22:14+05:006.033en-UScomputer systemssystems designclient serveroperating systemnetworksroutingatomicitynetwork securityfault toleranceauthenticationcryptographyUNIXmapreducedatabasesdistributed transactionsDNSvirtual machinesDARPAtracerouteRONDCTPPDPCDNBitTorrentBitcoinVoIPLDFWALDNSSECDDoSBotnetsTorMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.S095 Programming for the Puzzled (MIT)This class builds a bridge between the recreational world of algorithmic puzzles (puzzles that can be solved by algorithms) and the pragmatic world of computer programming, teaching students to program while solving puzzles. Python syntax and semantics required to understand the code are explained as needed for each puzzle.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s095-programming-for-the-puzzled-january-iap-2018
January IAP2018Devadas, Srini2018-05-10T15:32:01+05:006.S095en-USProgrammingCodingAlgorithmsPythonprogramming puzzlealgorithmic puzzleMerge sortQuicksortDivide and ConquerPivot-based portioningIn-place sortingRecursive functionRecursive fibonaccioptimizationenumerative searchiterative searchbacktracking searchbipartite graphsMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.0002 Introduction to Computational Thinking and Data Science (MIT)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.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016
Fall2016Grimson, EricGuttag, JohnBell, Ana2017-05-18T20:40:15+05:006.0002en-USPython 3.5Pythonmachine learningknapsack problemgreedy algorithmoptimizationweightsmodelscomputational thinkingdata sciencedynamic programmingrecursionknapsack problemexponential timestochasticrandomprobabilityindependent variablesdependent variablesmonte carlo simulationsimulationpopulation samplinglaw of large numbersvarianceconfidence intervalempirical rulestandard deviationcentral limit theoremempirical rulestandard deviationbiaserror distributionsamplingconfidence intervalerror barsnumpyscipymatplotlibpylabpythonplottinggraphingmachine learningsupervised learningcomputer modellingsignal-to-noisefeature vectorsclassification modelregression modelclassificationclassifiernearest neighborsfeature scalingdecision treesentropysupervised learningtraining dataclusteringcluster analysisunsupervised learningobjective functiondendogramstatistical fallacysystematic errorscorrelation and causationmisleading statisticsGIGOaxis truncatingstatistical fallacyextrapolationdata enhancementTexas Sharpshooter FallacyMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.453 Quantum Optical Communication (MIT)6.453 Quantum Optical Communication is one of a collection of MIT classes that deals with aspects of an emerging field known as quantum information science. This course covers Quantum Optics, Single-Mode and Two-Mode Quantum Systems, Multi-Mode Quantum Systems, Nonlinear Optics, and Quantum System Theory.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-453-quantum-optical-communication-fall-2016
Fall2016Shapiro, Jeffrey2017-03-09T18:55:43+05:006.453en-USQuantum optics: Dirac notation quantum mechanicsharmonic oscillator quantizationnumber states, coherent states, and squeezed statesradiation field quantization and quantum field propagationPrepresentation and classical fieldsLinear loss and linear amplification: Commutator preservation and the Uncertainty Principlebeam splittersphase-insensitive and phase-sensitive amplifiersQuantum photodetection: Direct detection, heterodyne detection, and homodyne detectionSecond-order nonlinear optics: Phasematched interactionsoptical parametric amplifiersgeneration of squeezed states, photon-twin beams, non-classical fourth-order interference, and polarization entanglementQuantum systems theory: optimum binary detectionquantum precision measurementsquantum cryptographyand quantum teleportationMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.0001 Introduction to Computer Science and Programming in Python (MIT)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.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016
Fall2016Bell, AnaGrimson, EricGuttag, John2017-01-31T21:26:04+05:006.0001en-USComputationBranchingIterationStringsGuess and checkApproximationsBisectionDecompositionAbstractionsFunctionsTuplesListsAliasingMutabilityRecursionDictionariesTestingDebuggingExceptionsAssertionsObject Oriented ProgrammingPython ClassesInheritanceProgram EfficiencySearchingSortingMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.005 Software Construction (MIT)6.005 Software Construction introduces fundamental principles and techniques of software development, i.e., how to write software that is safe from bugs, easy to understand, and ready for change. The course includes problem sets and a final project. Important topics include specifications and invariants; testing; abstract data types; design patterns for object-oriented programming; concurrent programming and concurrency; and functional programming. The 6.005 website homepage from Spring 2016, along with all course materials, is available to OpenCourseWare users.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-005-software-construction-spring-2016
Spring2016Miller, RobertGoldman, Max2017-01-31T21:23:39+05:006.005en-USSoftware ConstructionSoftware EngineeringStatic CheckingBasic JavaTestingCode ReviewVersion ControlSpecificationsDebuggingMutabilityImmutabilityRecursionAbstract Data TypesADTsInterfacesData TypesRegular Expressions and GrammarsParserGeneratorConcurrencyThread SafetyNetworkingQueuesLocksSynchronizationGUIGraphical User InterfacesMap filter reduceTeam version controlMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.881 Computational Personal Genomics: Making Sense of Complete Genomes (MIT)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.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-881-computational-personal-genomics-making-sense-of-complete-genomes-spring-2016
Spring2016Kellis, Manolis2017-01-10T19:15:22+05:006.881en-USGenomesNetworksEvolutioncomputational biologygenomicscomparative genomicsepigenomicsfunctional genomicsmotifsphylogenomicspersonal genomicsalgorithmsmachine learningbiologybiological datasetsproteomicssequence analysissequence alignmentgenome assemblynetwork motifsnetwork evolutiongraph algorithmsphylogeneticspythonprobabilitystatisticsentropyinformationMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.441 Information Theory (MIT)This is a graduate-level introduction to mathematics of information theory. We will cover both classical and modern topics, including information entropy, lossless data compression, binary hypothesis testing, channel coding, and lossy data compression.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-441-information-theory-spring-2016
Spring2016Polyanskiy, Yury2016-09-23T20:43:41+05:006.441en-USproperties of informationentropydivergenceinformation measuresmutual informationsufficient statisticmutual informationprobability of errorentropy ratelossless data compressionfixed-length compressionergodic sourcesuniversal compressionbinary hypothesis testinginformation projectionchannel codingachievability boundslinear codesgaussian channelsinput constraintslattice codeschannel codingenergy-per-bitsource-channel separationfeedbackforney concatenationlossy compressiondistortionmultiple-access channelrandom number generatorsource coding theoremnoisy communicationchannel coding theoremsource channel separation theorembroadcast channelsGaussian noisetime-varying channelsMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.042J Mathematics for Computer Science (MIT)This subject offers an interactive introduction to discrete mathematics oriented toward computer science and engineering. The subject coverage divides roughly into thirds: Fundamental concepts of mathematics: Definitions, proofs, sets, functions, relations. Discrete structures: graphs, state machines, modular arithmetic, counting. Discrete probability theory. On completion of 6.042J, students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.Interactive site components can be found on the Unit pages in the left-hand navigational bar, starting with Unit 1: Proofs.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015
Spring2015Meyer, Albert R.Chlipala, Adam2016-09-12T18:10:48+05:006.042J18.062Jen-US6.0426.042J18.062J18.062formal logic notationproof methodsinductionsetsrelationsgraph theoryinteger congruencesasymptotic notationgrowth of functionspermutationscombinationscountingdiscrete probabilityMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.820 Fundamentals of Program Analysis (MIT)This course offers a comprehensive introduction to the field of program analysis. It covers some of the major forms of program analysis including Type Checking, Abstract Interpretation and Model Checking. For each of these, the course covers the underlying theories as well as modern techniques and applications.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-820-fundamentals-of-program-analysis-fall-2015
Fall2015Solar-Lezama, Armando2016-07-20T21:02:54+05:006.820en-USprogram analysisLambda CalculusSemanticsλlet calculusHindley-Milner type inferenceMonadsAxiomatic SemanticsDataflow AnalysisType CheckingAbstract InterpretationModel CheckingMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.047 Computational Biology (MIT)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.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-fall-2015
Fall2015Kellis, Manolis2016-06-23T15:39:59+05:006.0476.878HST.507en-USGenomesNetworksEvolutioncomputational biologygenomicscomparative genomicsepigenomicsfunctional genomics, motifsphylogenomicspersonal genomicsalgorithmsmachine learningbiologybiological datasetsproteomicssequence analysissequence alignmentgenome assemblynetwork motifsnetwork evolutiongraph algorithmsphylogeneticspythonprobabilitystatisticsentropyinformationMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.046J Design and Analysis of Algorithms (MIT)This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015
Spring2015Demaine, ErikDevadas, SriniLynch, Nancy2016-03-04T22:07:15+05:006.046J18.410Jen-USalgorithmsortingsearch treesheapshashingdivide and conquerdynamic programminggreedy algorithmsamortized analysisgraph algorithmsshortest pathsnetwork flowcryptographyMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.231 Dynamic Programming and Stochastic Control (MIT)The 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. This includes systems with finite or infinite state spaces, as well as perfectly or imperfectly observed systems. We will also discuss approximation methods for problems involving large state spaces. Applications of dynamic programming in a variety of fields will be covered in recitations.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-231-dynamic-programming-and-stochastic-control-fall-2015
Fall2015Bertsekas, Dimitri2016-02-25T22:51:40+05:006.231en-USdynamic programmingstochastic controlalgorithmsfinite-statecontinuous-timeimperfect state informationsuboptimal controlfinite horizoninfinite horizondiscounted problemsstochastic shortest pathapproximate dynamic programmingMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.811 Principles and Practice of Assistive Technology (MIT)6.811: Principles and Practice of Assistive Technology (PPAT) is an interdisciplinary, project-based course, centered around a design project in which small teams of students work closely with a person with a disability in the Cambridge area to design a device, piece of equipment, app, or other solution that helps them live more independently.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-811-principles-and-practice-of-assistive-technology-fall-2014
Fall2014Li, WilliamTeo, GraceMiller, Robert2016-01-04T21:10:37+05:006.811en-USassistive technologydisabilityhuman-computer interfacecognitive impairmentscreen readerhead trackereye trackeruser centered designexperimental ethicsaccessibilityelectronicsMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.858 Computer Systems Security (MIT)6.858 Computer Systems Security is a class about the design and implementation of secure computer systems. Lectures cover threat models, attacks that compromise security, and techniques for achieving security, based on recent research papers. Topics include operating system (OS) security, capabilities, information flow control, language security, network protocols, hardware security, and security in web applications.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-858-computer-systems-security-fall-2014
Fall2014Zeldovich, Nickolai2015-07-15T20:26:00+05:006.858en-UScomputer system designsecure computer systemsthreat modelcomputer systems securityoperating systemoperating system securitycapabilitiesinformation flow controllanguage securitynetwork protocolshardware securitywebweb application securitysecure web serverweb applicationMIT OpenCourseWare https://ocw.mit.eduContent 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.htm6.890 Algorithmic Lower Bounds: Fun with Hardness Proofs (MIT)6.890 Algorithmic Lower Bounds: Fun with Hardness Proofs is a class taking a practical approach to proving problems can't be solved efficiently (in polynomial time and assuming standard complexity-theoretic assumptions like P ≠ NP). The class focuses on reductions and techniques for proving problems are computationally hard for a variety of complexity classes. Along the way, the class will create many interesting gadgets, learn many hardness proof styles, explore the connection between games and computation, survey several important problems and complexity classes, and crush hopes and dreams (for fast optimal solutions).
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-890-algorithmic-lower-bounds-fun-with-hardness-proofs-fall-2014
Fall2014Demaine, Erik2015-07-14T16:32:49+05:006.890en-USNP-completeness3SAT3-partitionHamiltonicityPSPACEEXPTIMEEXPSPACEgamespuzzlescomputationTetrisNintendoSuper Mario Bros.The Legend of ZeldaMetroidPokémonconstraint logicSudokuNikoliChessGoOthelloboard gamesinapproximabilityPCP theoremOPT-preserving reductionAPX-hardnessvertex coverSet-cover hardnessGroup Steiner treek-dense subgraphlabel coverUnique Games Conjectureindependent setfixed-parameter intractabilityparameter-preserving reductionW hierarchyclique-hardness3SUM-hardnessexponential time hypothesiscounting problemssolution uniquenessgame theoryExistential theory of the realsundecidabilityMIT OpenCourseWare https://ocw.mit.eduContent 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