Randomized algorithms are used in many areas, including algorithms for quick sort, primality testing and cryptography. Randomized algorithms are algorithms that use randomness as part of their logic or procedure. They can either always terminate with the correct answer (Las Vegas algorithms) or have a chance of producing an incorrect result (Monte Carlo algorithms). Randomized algorithms are used in many areas, such as sorting, primality testing, and cryptography.
Carnegie Mellon University
Fall 2022
A course offering rigorous study of computation, examining the central results and questions about the nature of computation, including finite automata, computational complexity, and cryptography.
No concepts data
+ 10 more conceptsStanford University
Winter 2023
This course provides an in-depth exploration of algorithm analysis and design. It covers various sorting, searching, and selection algorithms, data structures, and fundamental graph algorithms. It emphasizes the understanding of worst and average case analysis, recurrences, and asymptotics.
No concepts data
+ 30 more conceptsCarnegie Mellon University
Spring 2023
This course provides an initial dive into complexity theory, exploring computations bound by resources like time, space, and energy. Emphasis is placed on low complexity classes.
No concepts data
+ 29 more conceptsCarnegie Mellon University
Fall 2018
The course provides a rigorous introduction to the foundations of computer science, improving abstract thinking skills and preparing students to be innovators in the field. Topics include computation, computational complexity, and real-world applications of computational concepts. Prerequisites imply this is an intermediate-level course.
No concepts data
+ 25 more conceptsBrown University
Spring 2022
This analytical course dives into the mathematical underpinnings of computing successes like machine learning and cryptography, emphasizing the role of probability, randomness, and statistics. Students will explore mathematical models, theorems, and proofs. Practical implementations are not covered, focusing instead on the theories driving computational probabilities.
No concepts data
+ 10 more concepts