Teachers
Teaching Assistants
When and where Mondays 8.1512.00 in Bldg. 421, Aud. 73.
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, divideandconquer, and NPcompleteness (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  Slides  Weekplans  Materials 

External Memory I: I/O Model, Scanning, Sorting, and Searching.  1x1 · 4x1  External Memory I 


External Memory II: Bεtrees and String Btrees.  1x1 · 4x1  External Memory II 


External Memory III: CacheOblivious Model, Algorithms, and Data Structures.  1x1 · 4x1  External Memory III 


Approximate Data Structures I: Bloom filters.  Bloom Filters 


Approximate Data Structures II: Distance Oracles  Distance Oracles 


Approximate Data Structures III: Approximate Near Neighbor Search (Locality Sensitive Hashing)  1x1 · 4x1  LSH 


Distributed Computing I:  1x1  da1 


Distributed Computing II:  1x1 (v.2021) 1x1 (v.2020)  da2 


Distributed Computing III: Distributed Data Structures and Labeling Schemes.  1x1  Distributed Data Structures  Nearest Common Ancestors: A Survey and a New Algorithm for a Distributed Environment, S. Alstrup, C. Gavoille, H. Kaplan, T. Rauhe.  
Streaming I: Introduction, majority, MisraGries, Approx counting. 
1x1 · 4x1  Streaming I  
Streaming II: Approximate Counting and Frequency estimation. 
Streaming II 


Streaming III: Sketching, CountMin sketch. 
1x1 · 4x1  Streaming III 


Course Roundup, Questions, Future Perspectives 
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
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.