COMP 480

PROBABILISTIC ALGORITHMS AND DATA STRUCTURE

Credit Hours: 4 · Undergraduate Upper-Level

This course will be ideal for someone wanting to build a strong foundation in the theory and practice of algorithms for processing Big-Data. We will discuss advanced data structures and algorithms going beyond deterministic setting and emphasize the role of randomness in getting significant, often exponential, improvements in computations and memory. Graduate/Undergraduate Equivalency: COMP 580 .

Fall 2026

Topics

Universal hashing and collision handling
Concentration bounds
Bloom filters and cache compression
Consistent hashing and balanced allocation
Reservoir sampling and stream estimation
Count-Min and Count sketches
Locality-sensitive hashing and near-duplicate detection
Markov chains and MCMC
DNF counting
Agentic systems and efficient agent evaluation

Readings

Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Michael Mitzenmacher and Eli Upfal · Optional reference; source notes that no textbook covers all material

Prerequisites & course information

Short Title: PROB ALGORITHM DATA STRUCTURE Department: Computer Science Grade Mode: Standard Letter Course Type: Lecture Credit Hours: 4 Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students. Course Level: Undergraduate Upper-Level Prerequisite(s): COMP 382

Sources

Course catalogue ↗2026–2027
Official course syllabus or instructor course website ↗Fall 2026
Current public example independently checked against named reading references. Applies to the stated offering/section.