This course is taught by David Eppstein, with Parnian Shahkar as teaching assistant. There will be an online discussion forum on Ed Discussion, accessed through Canvas. Lectures will be held physically, Tuesdays and Thursdays, 12:30–1:50pm, in Social Ecology II, room 1304. Lecture notes will be linked from this page prior to each lecture. Except for students with special arrangements through the UCI Disability Services Center, all exams will be in person only.
I will assign weekly practice problem sets at the start of each week, covering that week's material, and I strongly recommend that all students do these, but they will not be collected and graded. Instead, solutions will be posted at the end of the week to "Resources" in Ed Discussion, and you can expect to see similar problems (or even in some cases the same problems) on the exams. There will be two exams (a midterm and a non-comprehensive final) each covering the material from roughly half of the class, each equally weighted in the overall course grade. Exams will be closed book, closed notes, and closed friends. Because of the late timing of the exam, regrade requests will not be accepted on the final exam.
There is no required textbook; however, much of the course material will be drawn from the Wikipedia articles collected together in the Wikipedia "book" Fundamental Data Structures. Additionally, suggested internet readings for the topics covered here will be linked from the schedule of topics.
Office hours: Parnian Shahkar, Thursdays 4:00 - 6:00, Zoom or in ICS1 458C (please use calendly to reserve time slots up to one hour in advance); David Eppstein, Tuesdays 2:30 - 3:30 in Bren 4082.
This course may be used as part of the comprehensive exam in the computer science masters program. To pass the comprehensive exam, students must earn at least an overall grade of A- in the course. If you are enrolled in the course, you will be automatically considered eligible for the comprehensive exam.
Tentative schedule of topics
- Week 1:
- The potential method of amortized analysis. Dynamic arrays. Double-ended queues.
- Week 2:
- The dictionary problem and hash tables.
- Hash functions: cryptographic hash functions, k-independent hashing, and tabulation hashing.
- Hash chaining, linear probing, and cuckoo hashing.
- Week 3:
- Representing sets by bitmaps and dictionaries.
Bloom filters and cuckoo filters.
Disjointness and the exponential time hypothesis. - Week 4:
- Streaming
data structures and frequent item detection.
Boyer–Moore majority voting, MinHash, and the count-min sketch.
Invertible Bloom filters. - Week 5:
- Priority queues. Binary heaps. k-ary heaps. Fibonacci heaps.
- Bucket queues. Priority queues vs integer sorting. Flat trees.
- Week 6:
- Midterm Tuesday, May 6.
- Binary
search trees and successor
queries.
- Splay trees, competitive analysis, and the dynamic optimality conjecture.
- Week 7:
- Range
searching, augmented
binary search
trees, and Dynamic prefix sums.
Ranking and unranking. Fractional cascading. - Week 8:
- Range
minima, Cartesian
trees, and lowest
common ancestors.
Level ancestors. Maintaining order in a list. - Week 9:
- Memorial day holiday, Monday, May 27
- Persistence and retroactivity.
- Week 10:
- Tries and suffix trees.
Union-find and dynamic graph algorithms. - Exam week:
- Final exam, Monday June 9, 1:30PM – 3:30PM in the same room as the lectures.
See also Erik Demaine's open courseware class on similar material.