Explore advanced topics in the design and analysis of algorithms and data structures. The focus is on expanding your toolkit of problem-solving techniques and considering new settings that model real-world challenges. Topics may include: randomization, approximation algorithms, online and streaming settings, parallel and distributed computing, linear programming and LP rounding, optimization under uncertainty, bias and fairness in algorithms, and the algorithmic foundations of data science, machine learning, and operations research.

Units: 1

Max Enrollment: 18

Prerequisites: CS 231 or permission of the instructor.

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes: