Advanced Algorithms: Greedy Methods & Dynamic Programming
This course is part of Algorithms Specialization.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Algorithms Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Master greedy algorithm design and implementation
Implement and analyze minimum spanning tree algorithms
Develop efficient dynamic programming solutions
Solve complex optimization problems
Analyze algorithm correctness and efficiency
Skills you'll gain
This course includes:
9.3 Hours PreRecorded video
9 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers advanced algorithmic techniques focusing on greedy algorithms, minimum spanning trees, and dynamic programming. Students learn through practical applications including scheduling, clustering, Huffman codes, knapsack problems, and sequence alignment. The curriculum provides hands-on experience implementing these algorithms while exploring their theoretical foundations. Through programming assignments and quizzes, learners develop expertise in solving complex computational problems using efficient algorithmic approaches.
Two motivating applications; selected review; introduction to greedy algorithms
Module 1 · 3 Hours to complete
Kruskal's MST algorithm and applications to clustering
Module 2 · 4 Hours to complete
Huffman codes; introduction to dynamic programming
Module 3 · 2 Hours to complete
Advanced dynamic programming
Module 4 · 3 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Algorithms Specialization
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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