General Info


Teaching Assistants

When and where Mondays 8.15-12.00 in Bldg. 421, Aud. 71.

Prerequisites Undergraduate level courses in algorithms and data structures (comparable to 02105 + 02110) and mathematical maturity. You should have a working knowledge of algorithm analysis (e.g. asymptotic notation, worst case analysis, amortized analysis, basic analysis of randomized algorithms), data structures (e.g. trees, heaps, priority queues, hash tables, balanced binary search trees), graph algorithms (e.g. BFS, DFS, single source shortest paths, minimum spanning trees, topological sorting), dynamic programming, divide-and-conquer, and NP-completeness (e.g. basic reductions).


The weekplan is preliminary. It will be updated during the course. Under each week there is a number of suggestions for reading material regarding that weeks lecture. It is not the intention that you read all of the papers. It is a list of papers and notes where you can read about the subject discussed at the lecture.

Week Topics Teacher Slides Weekplans Materials
Streaming I:
Introduction, majority, Misra-Gries, Approx counting.
Inge 1x1 · 4x1 Streaming I
Hashing Eva 1x1 Hashing
Streaming II:
Approximate Counting and Frequency estimation.
Eva 1x1 Streaming II
Streaming III:
Sketching, CountMin sketch.
Inge 1x1 · 4x1 Streaming III
Approximate Data Structures I: Distance Oracles Eva Distance Oracles
Approximate Data Structures II: Bloom filters. Inge Bloom Filters
Approximate Data Structures III: Approximate Near Neighbor Search (Locality Sensitive Hashing) Inge 1x1 · 4x1 LSH
Distributed Computing I: Eva 1x1 da1
Distributed Computing II: Eva 1x1 (v.2021) 1x1 (v.2020) da2
Distributed Computing III: Eva da 3 We read: Linial's Lower Bound Made Easy" by Laurinharju and Soumela (Podc'14) and Sinkless Orientation Made Simple by Balliu et al (Sosa'23)
Massively Parallel Computing Eva 1x1 · 4x1 mpc
Massively Parallel 2: spanning trees Eva 1x1 Massively Parallel Algorithms, M. Ghaffari, 2019, Chapter 3 (Graph sketching, connectivity).
Course Roundup, Questions, Future Perspectives Eva & Inge N/A N/A Note: class starts 9:15.

Mandatory Exercises

The mandatory exercise will be posted circa Nov 6th and answers are due circa Nov 15th.

Use the template.tex file to prepare your write up your solution to the exercises. Do not repeat the problem statement in your solutions and do not modify the template. Compile your solutions using LaTeX. The maximum size of the finished pdf must be at most 2 pages. To submit your solution:

Collaboration policy for mandatory exercises

Violation of the collaboration policy is strictly prohibited.


Frequently Asked Questions

How can I access the listed reading material? Why are some of the links are behind a paywall? We typically link to material using standard doi's or publication venue links. We do this since these are stable over time and allow you to uniquely identify the material. To access these (at no cost) you sometimes need to use academic search engines or library services provided by DTU. Ask your teacher if you are unfamiliar with how to use these tools.

What do I do if I want to do a MSc/BSc thesis or project in Algorithms? Great! Algorithms is an excellent topic to work on :-) and Algorithms for Massive Data Sets is designed to prepare you to write a strong thesis. Some basic tips and points.