Algorithm Basics: Divide-Conquer, Sorting & Randomization
Master essential algorithm techniques including asymptotic analysis, divide-and-conquer strategies, and randomized algorithms for efficient problem solving.
Master essential algorithm techniques including asymptotic analysis, divide-and-conquer strategies, and randomized algorithms for efficient problem solving.
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
Analyze algorithm efficiency using asymptotic notation and runtime analysis
Implement and apply divide-and-conquer strategies to solve complex problems
Master sorting algorithms including MergeSort and QuickSort with their analysis
Design randomized algorithms for optimization and selection problems
Apply graph algorithms to solve cut problems
Prove algorithm correctness and analyze time complexity
Skills you'll gain
This course includes:
15 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores fundamental algorithm design paradigms with a focus on divide-and-conquer techniques, sorting and searching algorithms, and randomized approaches. Students begin by mastering asymptotic notation and analysis, essential for evaluating algorithm efficiency. The curriculum then delves into key divide-and-conquer strategies, including MergeSort, Karatsuba multiplication, and matrix multiplication algorithms. The course covers the master method for analyzing recursive algorithms and introduces randomized techniques like QuickSort and the contraction algorithm for graph minimum cuts. Throughout the course, theoretical concepts are reinforced with programming assignments that provide hands-on experience implementing these algorithms. By the end, students will have developed a solid foundation in algorithmic thinking and gained practical skills in designing efficient solutions to complex computational problems.
Week 1
Module 1 · 3 Hours to complete
Week 2
Module 2 · 4 Hours to complete
Week 3
Module 3 · 3 Hours to complete
Week 4
Module 4 · 4 Hours to complete
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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