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This course provides an overview of various topics in algorithms based on a selection of materials on exact, geometric, randomized, and approximation algorithms, as well as advanced data structures. As such, this course serves as a foundation for the associated master's level courses in algorithms (see: https://www.informatik.uni-wuerzburg.de/algo/lehre/). The course covers improvements to classical algorithms as well as approaches to tackle NP-hard problems. These approaches range from understanding "good" algorithms that solve such problems exactly, to efficient algorithms that approximate such problems, to randomized approaches that work well in expected value. Along the way, we will learn about some interesting data structures that can be exploited for this purpose.
At the end of this course, students should have a rough overview of advanced topics in algorithms and data structures. They should be able to analyze and design algorithms of any type and understand the appropriate use of data structures.
Languages: English (slides, lectures, exercises), German (lectures & exercises for German-speaking audience) Lectures: Wednesday, 14:15–15:45, ÜR I (Informatikgebäude) Exercises: Monday, 16:00–17:30, HS 4 (Naturwissenschaftlicher Hörsaalbau), first exercise on Monday, 23.10.2023 Docents: Johannes Zink (Lectures), Oksana Firman (Exercises) Oral exams: Tuesday, 27.02.2024 and Wednesday, 17.04.2024
Please do not forget to register in WueStudy (Ausgewählte Kapitel der Algorithmik/Theorie/Informatik [Selected Topics in ...]). The deadlines are January 31 and March 31, respectively.
Language of the oral exam is English or German. It will take approx. 20 minutes per candidate.
A grade bonus of 0.3 on the final grade will be given to students who score at least 50% on the exercise sheets.Credits: 5 ECTS, 2+2 SWS Premises: Algorithms and Data Structures (recommended), Algorithmic Graph Theory (recommended) Target audience: Master Computer Science, Master Mathematics, Master Computational Mathematics Registration:
Please enroll into this WueCampus course room: use the rightmost item in the bar below the course title: "Mich in diesem Kurs einschreiben".
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