Combinatorics and Probability
Master fundamental concepts of counting and probability theory with practical applications in computer science and algorithms.
Master fundamental concepts of counting and probability theory with practical applications in computer science and algorithms.
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
Master fundamental counting principles and combinatorial techniques
Understand and apply probability theory to real-world problems
Develop skills in mathematical modeling and problem-solving
Implement probability concepts in Python programming
Analyze and solve complex combinatorial problems
Skills you'll gain
This course includes:
3.37 Hours PreRecorded video
47 assignments
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FullTime access
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There are 6 modules in this course
This comprehensive course covers fundamental concepts in combinatorics and probability theory, essential for computer science and algorithms. Starting with basic counting principles and progressing through binomial coefficients, the course explores probability spaces, random variables, and their practical applications. Students learn through theoretical foundations and hands-on programming exercises, culminating in a practical project implementing a strategic dice game that demonstrates the counterintuitive nature of probability.
Basic Counting
Module 1 · 4 Hours to complete
Binomial Coefficients
Module 2 · 3 Hours to complete
Advanced Counting
Module 3 · 3 Hours to complete
Probability
Module 4 · 4 Hours to complete
Random Variables
Module 5 · 3 Hours to complete
Project: Dice Games
Module 6 · 2 Hours to complete
Fee Structure
Instructor
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.
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