Introduction to Graph Theory
This course is part of Introduction to Discrete Mathematics for Computer Science.
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 Introduction to Discrete Mathematics for Computer Science 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
Understand fundamental graph theory concepts and applications
Solve real-world problems using graph-based approaches
Implement basic graph algorithms in Python
Analyze network structures and properties
Apply graph theory to practical engineering challenges
Skills you'll gain
This course includes:
5.7 Hours PreRecorded video
30 assignments
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FullTime access
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There are 5 modules in this course
This comprehensive course introduces the fundamentals of graph theory and its practical applications. Students learn about various graph types, properties, and algorithms through real-world examples including GPS systems, integrated circuit design, and genome assembly. The curriculum covers essential topics such as graph coloring, network flows, matching algorithms, and Ramsey theory, culminating in the implementation of the Nobel Prize-winning Gale-Shapley algorithm.
What is a Graph?
Module 1 · 3 Hours to complete
Cycles
Module 2 · 5 Hours to complete
Graph Classes
Module 3 · 3 Hours to complete
Graph Parameters
Module 4 · 3 Hours to complete
Flows and Matchings
Module 5 · 4 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: Introduction to Discrete Mathematics for Computer Science
Instructors
13 Courses
Distinguished Computer Scientist and Algorithms Expert
Alexander S. Kulikov serves as a visiting professor at the University of California, San Diego, and a leading research fellow at the Steklov Institute of Mathematics in St. Petersburg. His academic journey includes earning his Ph.D. in 2009 and Dr.Sci. in 2017 from the St. Petersburg Department of Steklov Institute of Mathematics. His research focuses on algorithms for NP-hard problems and circuit complexity, with significant contributions to computational complexity theory and algorithm design. He has authored several influential educational resources, including "Learning Algorithms Through Programming and Puzzle Solving" and co-created major online courses on platforms like Coursera and edX. His teaching experience spans more than eight years, during which he has developed innovative approaches to algorithms education. Currently at JetBrains as a researcher, he continues to advance the field through his work on algorithmic problem-solving and computational complexity, while maintaining his academic connections through his visiting professorship at UCSD and research position at Steklov Institute.
Leading Researcher in Computational Complexity
Senior research fellow at the Steklov Institute of Mathematics. Associate professor at the HSE Faculty of Computer Science. Graduate of the Faculty of Mechanics and Mathematics at Moscow State University, PhD in Physics and Mathematics.
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Frequently asked questions
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