Please generally refer to the information on this website and in the eCampus course, since the information on basis is not reliably correct.

The will be another Q&A tutorial on March 27, 15:00-18:00 in Room 2.050 in the CS building. If you have any questions or topics you would like to discuss, please send an email to Michael Kaibel .

The second exam phase is coming up. The oral exams will be held on March 30-31, 2026. If you want to participate (and fulfill the admission requirements), you must register via BASIS until a week before the exam, and write an email with subject 'ADA Exams' to Christiane Stuke until March 9, 2026 to make an appointment for an exam time slot. For more information, please consult the eCampus page.

We will explore advanced techniques for the design and the analysis of algorithms for data analysis and big data. We will focus on selected examples from classical as well as modern algorithmics. Our discussion will include both polynomial time and exponential time exact algorithms, as well as approximation algorithms for combinatorial optimization problems (often on graphs) and their analysis.

Topics will include algorithms and their analysis for graph similarity (e.g., (sub)graph isomorphism, Weisfeiler-Leman), efficient centrality computation for networks, streaming algorithms, I/O efficient algorithms, and data structures such as, e.g., Fibonacci heaps, Union-Find, and their analysis.

Prerequisites:

Note that knowledge of foundations of algorithms and data structures is essential as prerequisite. In particular, we expect you to have a solid foundation in the analysis of algorithms and data structures, including concepts such as Big-O notation and basic algorithmic analysis, as typically taught in an Introduction to Algorithms course. You should also have programming experience — while we do not require proficiency in a specific programming language, the ability to implement algorithms and data structures is essential. Familiarity with basic computational complexity concepts is important. You should understand what it means for a problem to be NP-hard or solvable in polynomial time, and be comfortable working with such classifications. We assume you are acquainted with fundamental data structures and algorithms, including binary heaps, priority queues, and others. You should be able to analyze them (in terms of correctness and worst-case running time) and apply them efficiently. As many of the techniques involve graphs, you should know standard graph algorithms and operations, such as Breadth-First Search (BFS), Depth-First Search (DFS), and shortest path algorithms like Dijkstra’s algorithm. All of this knowledge is typically covered in an introductory algorithms course. Additionally, a basic understanding of mathematics is necessary, including proof techniques such as induction, as well as familiarity with vectors and matrices.

The lecture will be complemented by tutorials led by Charlotte Bockhorst and Michael Kaibel, with exercises coordinated by Laura Bülte. There are four alternative appointments. You only need to attend one of the appointments. The assignment of the attendees via TVS is now closed. If there was a problem, or you could not register, please contact Laura Bülte.

The exercise sheets will be uploaded to eCampus on Tuesday afternoons, and will be discussed in the tutorials one week later.

There will be oral exams in WiSe 2025/26.

The first exam period will be on February 5-6 and February 9-11. For more information, consult the eCampus page.

Admission to the exam requires actively participating in the tutorials (the official admission requirements can be found in the Module Handbook and on eCampus). In addition, we’ll be holding two written tests during lecture time this semester. To qualify for the exam, you need to pass one of them. Dates for the tests: November 10, 2025 and December 8, 2025 (during the lecture).

The second exam period will be March 30-31, 2026.