Advanced Algorithm Design and Analysis
Master advanced algorithmic concepts including greedy algorithms, dynamic programming, and NP-completeness.
Master advanced algorithmic concepts including greedy algorithms, dynamic programming, and NP-completeness.
This comprehensive course explores advanced concepts in algorithm design and analysis, building upon fundamental programming knowledge. Students will dive deep into greedy algorithms for scheduling and clustering, dynamic programming techniques for optimization problems, and the theoretical foundations of NP-completeness. The curriculum combines rigorous theoretical concepts with practical implementation through programming assignments. Focusing on conceptual understanding over low-level details, the course provides mastery of algorithmic thinking comparable to graduate-level education. Students will implement algorithms in their preferred programming language and complete multiple-choice assessments to reinforce learning.
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What you'll learn
Master advanced greedy algorithms for scheduling and clustering problems
Implement dynamic programming solutions for optimization challenges
Understand NP-completeness and its implications for algorithm design
Analyze heuristics and local search techniques
Develop practical programming skills through hands-on algorithm implementation
Skills you'll gain
This course includes:
PreRecorded video
6 programming assignments, 6 problem sets, final exam
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Limited Access access
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Module Description
This advanced algorithms course covers sophisticated techniques in algorithm design and analysis. The curriculum includes comprehensive coverage of greedy algorithms, dynamic programming, and NP-completeness. Students learn through a combination of theoretical instruction and practical programming assignments. The course emphasizes conceptual understanding while providing hands-on experience with implementing various algorithms. Topics include scheduling, minimum spanning trees, clustering, Huffman codes, knapsack problems, and sequence alignment.
Fee Structure
Instructor
A Pioneering Computer Scientist and Game Theory Expert
Tim Roughgarden has established himself as a leading figure in theoretical computer science, particularly at the intersection of algorithms and economics. Born July 20, 1975, he earned his Ph.D. from Cornell University in 2002 under Éva Tardos's supervision, followed by a postdoc at UC Berkeley. He served as a professor in Stanford University's Computer Science department from 2004 to 2018 before joining Columbia University. His research focuses on algorithm design, game theory, and their applications to networks, auctions, and blockchains. His contributions have earned him numerous prestigious honors, including the Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers, the Gödel Prize, and a Guggenheim Fellowship. As an educator, he has developed widely-used online courses in algorithms through Coursera and authored several influential textbooks including "Algorithms Illuminated" and "Twenty Lectures on Algorithmic Game Theory." Currently serving as a Professor at Columbia University and Head of Research at a16z crypto, he continues to advance the field through his work on the boundary of computer science and economics.
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Frequently asked questions
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