Analysis of Algorithms
Master advanced algorithm analysis techniques for large combinatorial structures.It covers generating functions and real asymptotics.
Master advanced algorithm analysis techniques for large combinatorial structures.It covers generating functions and real asymptotics.
This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. It covers generating functions and real asymptotics, then introduces the symbolic method for analyzing algorithms and basic structures like permutations, trees, strings, words, and mappings. The course offers a deep dive into analytic combinatorics, providing tools for sophisticated algorithm analysis. Designed for those with prior algorithm knowledge, it offers a rigorous mathematical approach to understanding and predicting algorithm behavior in complex scenarios.
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English
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What you'll learn
Understand the historical context and motivation for scientific algorithm analysis
Master recurrence relations and their application in algorithm analysis
Apply generating functions to solve complex combinatorial problems
Develop skills in asymptotic analysis for algorithm performance prediction
Learn the fundamentals of analytic combinatorics and its applications
Analyze advanced data structures like trees, tries, and permutations
Skills you'll gain
This course includes:
11.5 Hours PreRecorded video
11 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 9 modules in this course
This course offers a comprehensive exploration of advanced algorithm analysis techniques. It begins with an introduction to the scientific study of algorithm performance, using Quicksort as a classic example. The curriculum then delves into recurrence relations, generating functions, and asymptotics, providing a mathematical foundation for precise algorithm analysis. Students will learn about analytic combinatorics, a systematic approach to studying combinatorial structures. The course covers various data structures and algorithms, including trees, permutations, strings, tries, and mappings, applying analytic combinatorics to understand their properties and performance. Throughout the course, students will gain practical skills in predicting and analyzing the behavior of complex algorithms and data structures.
Analysis of Algorithms
Module 1 · 2 Hours to complete
Recurrences
Module 2 · 3 Hours to complete
Generating Functions
Module 3 · 2 Hours to complete
Asymptotics
Module 4 · 2 Hours to complete
Analytic Combinatorics
Module 5 · 2 Hours to complete
Trees
Module 6 · 2 Hours to complete
Permutations
Module 7 · 2 Hours to complete
Strings and Tries
Module 8 · 2 Hours to complete
Words and Mappings
Module 9 · 2 Hours to complete
Fee Structure
Instructor
Prominent Computer Scientist and Educator
Robert Sedgewick is the William O. Baker Professor of Computer Science, Emeritus, at Princeton University, where he also served as the founding chair of the Department of Computer Science. He obtained his Ph.D. from Stanford University in 1975 and has previously held faculty positions at Brown University. His research includes analytic combinatorics, algorithm design, and the scientific analysis of algorithms, complemented by his involvement in curriculum development and innovative educational practices. Sedgewick has held visiting research roles at notable institutions such as Xerox PARC and INRIA and is a former board member of Adobe Systems. He is a prolific author, having published numerous books in his field, including influential textbooks that have shaped computer science education globally.
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4.4 course rating
953 ratings
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