Design & Analysis Comp Algorit

Catalog description

Efficient algorithms and data structures are the foundation of all modern computing, including large-scale data processing, the web, AI and machine learning. This course covers the foundational paradigms and techniques for the design and analysis of algorithms and prepares students for advanced topics across computer science. Algorithm design paradigms include divide-and-conquer, dynamic programming, greedy methods, backtracking, and branch-and-bound techniques as well as probabilistic and parallel algorithms that take advantage of advances in computer hardware. Analysis techniques focus on time- and space-complexity, and average- and worst-case analysis, with an introduction to NP-completeness and the use of approximation algorithms to deal with NP-hardness. To build genuine problem-solving abilities and solid theoretical and practical foundations in algorithm design, this course prohibits the use of AI in course assignments except in targeted assignments that explore and critically evaluate the role of AI in algorithm design and problem solving. Prerequisites: CS 375 or equivalent. Offered every semester when possible.