When and where Mondays 8.15-12.00 in Bldg. 358, Room 060a.
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).
Gradescope We use Gradescope for correcting and scoring the mandatory exercises. The system significantly improves consistency and quality in correcting. Please sign up for Gradescope as follows:
86JWX7, your full name, your
@student.dtu.dkemail, and your student-id (of the form
s123456). Please follow these instructions precisely so that we can correctly identify you.
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.
|Introduction and Warm-up||Warm Up|
|External Memory I: I/O Model, Scanning, Sorting, and Searching.||1x1 · 4x1||External Memory I||
||External Memory I 1 · External Memory I 2|
|External Memory II: Bε-trees and String B-trees.||1x1 · 4x1||External Memory II||
||External Memory II 1 · External Memory II 2|
|External Memory III: Cache-Oblivious Model, Algorithms, and Data Structures.||1x1 · 4x1||External Memory III||External Memory III 1 · External Memory III 2|
|Approximate Data Structures I: Bloom filters.||Bloom Filters||
|Approximate Data Structures II: Approximate Near Neighbor Search (Locality Sensitive Hashing)||1x1 · 4x1||LSH||
|Approximate Data Structures III: Distance Oracles||
Introduction, majority, Misra-Gries, Approx counting.
|1x1 · 4x1||Streaming I|
Approximate Counting and Frequency estimation.
|1x1 · 4x1||Streaming II|
Sketching, CountMin sketch.
|1x1 · 4x1||Streaming III|
|Distributed Computing I:||
|Distributed Computing II:||
|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.
How should I write my mandatory exercises? The ideal writing format for mandatory exercises is classical scientific writing, such as the writing found in the peer-reviewed articles listed as reading material for this course (not textbooks and other pedagogical material). One of the objectives of this course is to practice and learn this kind of writing. A few tips:
How much do the mandatory exercises count in the final grade? The final grade is an overall evaluation of your mandatory exercise and the oral exam combined. Thus, there is no precise division of these part in the final grade. However, expect that (in most cases, and under normal circumstances) the mandatory exercises account for a large fraction of the final grade.
Can I write my assignments in Danish? Ja. Du er meget velkommen til at aflevere på dansk.
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.