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RSS Feed for graph algorithms6.852J Distributed Algorithms (MIT) 6.852J Distributed Algorithms (MIT)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
http://dspace.mit.edu/handle/1721.1/60694
http://dspace.mit.edu/handle/1721.1/606946.046J Introduction to Algorithms (SMA 5503) (MIT) 6.046J Introduction to Algorithms (SMA 5503) (MIT)This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5503 Analysis and Design of Algorithms This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5503 Analysis and Design of Algorithms
http://dspace.mit.edu/handle/1721.1/37150
http://dspace.mit.edu/handle/1721.1/371506.046J Introduction to Algorithms (MIT) 6.046J Introduction to Algorithms (MIT)This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing
http://dspace.mit.edu/handle/1721.1/36847
http://dspace.mit.edu/handle/1721.1/368476.852J Distributed Algorithms (MIT) 6.852J Distributed Algorithms (MIT)6 852J 18 437J 160 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 160 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
http://dspace.mit.edu/handle/1721.1/36405
http://dspace.mit.edu/handle/1721.1/364056.046J Introduction to Algorithms (SMA 5503) (MIT) 6.046J Introduction to Algorithms (SMA 5503) (MIT)Includes audio video content AV lectures This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5503 Analysis and Design of Algorithms Includes audio video content AV lectures This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5503 Analysis and Design of Algorithms
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-20056.046J Design and Analysis of Algorithms (MIT) 6.046J Design and Analysis of Algorithms (MIT)Techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics include sorting search trees heaps and hashing divide and conquer dynamic programming greedy algorithms amortized analysis graph algorithms and shortest paths Advanced topics may include network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing Techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics include sorting search trees heaps and hashing divide and conquer dynamic programming greedy algorithms amortized analysis graph algorithms and shortest paths Advanced topics may include network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2012
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-20126.047 Computational Biology: Genomes, Networks, Evolution (MIT) 6.047 Computational Biology: Genomes, Networks, Evolution (MIT)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
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-genomes-networks-evolution-fall-2008
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-genomes-networks-evolution-fall-20086.856J Randomized Algorithms (MIT) 6.856J Randomized Algorithms (MIT)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
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-856j-randomized-algorithms-fall-2002
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-856j-randomized-algorithms-fall-20026.895 Computational Biology: Genomes, Networks, Evolution (MIT) 6.895 Computational Biology: Genomes, Networks, Evolution (MIT)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
http://dspace.mit.edu/handle/1721.1/55901
http://dspace.mit.edu/handle/1721.1/559011.204 Computer Algorithms in Systems Engineering (MIT) 1.204 Computer Algorithms in Systems Engineering (MIT)This course covers concepts of computation used in analysis of engineering systems It includes the following topics data structures relational database representations of engineering data algorithms for the solution and optimization of engineering system designs greedy dynamic programming branch and bound graph algorithms nonlinear optimization and introduction to complexity analysis Object oriented efficient implementations of algorithms are emphasized This course covers concepts of computation used in analysis of engineering systems It includes the following topics data structures relational database representations of engineering data algorithms for the solution and optimization of engineering system designs greedy dynamic programming branch and bound graph algorithms nonlinear optimization and introduction to complexity analysis Object oriented efficient implementations of algorithms are emphasized
http://ocw.mit.edu/courses/civil-and-environmental-engineering/1-204-computer-algorithms-in-systems-engineering-spring-2010
http://ocw.mit.edu/courses/civil-and-environmental-engineering/1-204-computer-algorithms-in-systems-engineering-spring-20106.046J Introduction to Algorithms (SMA 5503) (MIT)This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5503 Analysis and Design of Algorithms
http://www.snow.or.kr/lecture/applied_sciences/computer_science/532.html
http://www.snow.or.kr/lecture/applied_sciences/computer_science/532.html6.046J Design and Analysis of Algorithms (MIT) 6.046J Design and Analysis of Algorithms (MIT)Includes audio video content AV lectures 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 Includes audio video content AV lectures 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
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015RES.LL-005 D4M: Signal Processing on Databases (MIT) RES.LL-005 D4M: Signal Processing on Databases (MIT)Includes audio video content AV lectures D4M is a breakthrough in computer programming that combines graph theory linear algebra and databases to address problems associated with Big Data Search social media ad placement mapping tracking spam filtering fraud detection wireless communication drug discovery and bioinformatics all attempt to find items of interest in vast quantities of data This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms group theory and database design This approach has been implemented in software The class will begin with a number of practical problems introduce the appropriate theory and then apply the theory to these problems Students will apply these ideas in the final project of their Includes audio video content AV lectures D4M is a breakthrough in computer programming that combines graph theory linear algebra and databases to address problems associated with Big Data Search social media ad placement mapping tracking spam filtering fraud detection wireless communication drug discovery and bioinformatics all attempt to find items of interest in vast quantities of data This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms group theory and database design This approach has been implemented in software The class will begin with a number of practical problems introduce the appropriate theory and then apply the theory to these problems Students will apply these ideas in the final project of their
http://ocw.mit.edu/resources/res-ll-005-d4m-signal-processing-on-databases-fall-2012
http://ocw.mit.edu/resources/res-ll-005-d4m-signal-processing-on-databases-fall-20126.047 Computational Biology (MIT) 6.047 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 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
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-fall-2015
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-fall-20156.852J Distributed Algorithms (MIT)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
https://dspace.mit.edu/handle/1721.1/60694
https://dspace.mit.edu/handle/1721.1/606946.046J Introduction to Algorithms (SMA 5503) (MIT)This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5503 Analysis and Design of Algorithms
https://dspace.mit.edu/handle/1721.1/37150
https://dspace.mit.edu/handle/1721.1/371506.046J Introduction to Algorithms (MIT)This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing
https://dspace.mit.edu/handle/1721.1/36847
https://dspace.mit.edu/handle/1721.1/368476.852J Distributed Algorithms (MIT)6 852J 18 437J 160 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
https://dspace.mit.edu/handle/1721.1/36405
https://dspace.mit.edu/handle/1721.1/364056.046J Design and Analysis of Algorithms (MIT)Techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics include sorting search trees heaps and hashing divide and conquer dynamic programming greedy algorithms amortized analysis graph algorithms and shortest paths Advanced topics may include network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2012
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-20126.046J Introduction to Algorithms (SMA 5503) (MIT)This course teaches techniques for the design and analysis of efficient algorithms emphasizing methods useful in practice Topics covered include sorting search trees heaps and hashing divide and conquer dynamic programming amortized analysis graph algorithms shortest paths network flow computational geometry number theoretic algorithms polynomial and matrix calculations caching and parallel computing This course was also taught as part of the Singapore MIT Alliance SMA programme as course number SMA 5503 Analysis and Design of Algorithms
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-20056.856J Randomized Algorithms (MIT)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
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-856j-randomized-algorithms-fall-2002
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-856j-randomized-algorithms-fall-2002AlgorithmsThis course focuses on the fundamentals of computer algorithms emphasizing methods useful in practice This free course may be completed online at any time See course site for detailed overview and learning outcomes Computer Science 303
http://dspace.jorum.ac.uk/xmlui/handle/10949/16459
http://dspace.jorum.ac.uk/xmlui/handle/10949/164596.895 Computational Biology: Genomes, Networks, Evolution (MIT)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
https://dspace.mit.edu/handle/1721.1/55901
https://dspace.mit.edu/handle/1721.1/559011.204 Computer Algorithms in Systems Engineering (MIT)This course covers concepts of computation used in analysis of engineering systems It includes the following topics data structures relational database representations of engineering data algorithms for the solution and optimization of engineering system designs greedy dynamic programming branch and bound graph algorithms nonlinear optimization and introduction to complexity analysis Object oriented efficient implementations of algorithms are emphasized
https://ocw.mit.edu/courses/civil-and-environmental-engineering/1-204-computer-algorithms-in-systems-engineering-spring-2010
https://ocw.mit.edu/courses/civil-and-environmental-engineering/1-204-computer-algorithms-in-systems-engineering-spring-20106.047 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
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-047-computational-biology-fall-2015RES.LL-005 D4M: Signal Processing on Databases (MIT)D4M is a breakthrough in computer programming that combines graph theory linear algebra and databases to address problems associated with Big Data Search social media ad placement mapping tracking spam filtering fraud detection wireless communication drug discovery and bioinformatics all attempt to find items of interest in vast quantities of data This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms group theory and database design This approach has been implemented in software The class will begin with a number of practical problems introduce the appropriate theory and then apply the theory to these problems Students will apply these ideas in the final project of their choosing The course will contain a number
https://ocw.mit.edu/resources/res-ll-005-d4m-signal-processing-on-databases-fall-2012
https://ocw.mit.edu/resources/res-ll-005-d4m-signal-processing-on-databases-fall-20126.046J 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
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-20156.881 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
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-881-computational-personal-genomics-making-sense-of-complete-genomes-spring-2016