The reading introduces concepts of probability theory in algorithmic context. In a way, this project delivers probability “on demand”. The student will learn about leveraging randomness for speeding up computations.
Instructor: Alperen Ergur, Department of Mathematics, University of Texas at San Antonio
- Reference: Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis by Michael Mitzenmacher and Eli Upfal
- Prerequisites: Linear Algebra, Calculus 1-2, and familiarity with Discrete Mathematics on a basic level.
- Meetings: Biweekly
- Deadline: CLOSED
- Type: Advanced Undergraduate, Beginning Graduate, Reading, Research
- Size: 3 Students
PROJECT CLOSED. Thank you for your interest!
Mentored Reading Program 