Universität Wien
Warning! The directory is not yet complete and will be amended until the beginning of the term.

051024 VU Algorithms and Data Structures 1 (2026W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
Tu 06.10. 08:00-11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Register/Deregister for this course

Details

max. 50 participants
Language: German, English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 13.10. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 20.10. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 27.10. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 03.11. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 10.11. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 17.11. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 24.11. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 01.12. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 15.12. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 12.01. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 19.01. 08:00 - 11:15 Hörsaal 32 Hauptgebäude, 1.Stock, Stiege 9
  • Tuesday 26.01. 08:00 - 09:30 Auditorium Maximum Tiefparterre Hauptgebäude Stiege 10
  • Tuesday 02.02. 08:00 - 11:15 Auditorium Maximum Tiefparterre Hauptgebäude Stiege 10

Information

Aims, contents and method of the course

Learning goals
• Develop a clear understanding of the computational model (via C++).
• Analyze running time through the study of sorting algorithms.
• Build clean mental models of fundamental data structures and gain fluency with standard implementations.
• Deepen algorithm design and analysis skills through graph and optimization problems.

Contents
• Introduction to the computational model
• Sorting algorithms
• Lists, stacks, and queues
• Hashing
• Trees: heaps and search trees
• Graphs: terminology review, key algorithms, and proofs of correctness

Language and attendance
• The course will be taught in English.
• On-site in-class attendance at the first lecture is mandatory.

Tutorials and homework
• Homework will be solved independently and discussed in tutorials.
• While voluntary, attending the tutorial is strongly recommended to consolidate course concepts.

Prerequisites
• Basic knowledge of mathematics and C++ programming as taught in the StEOP.

Mathematics prerequisites (MG1)
• Functions
• Proofs
• Induction
• Graph theory

Programming prerequisites (PR1)
• Basic C++
• Understanding the computational model for a C++ program:
– Control flow
– Function calls
– Local variables
– Loops (and related constructs)

Assessment and permitted materials

During the semester, there will be four short online exams, each lasting 45 minutes. These will be conducted via Moodle and can be taken within a fixed, almost-24-hour, window announced for each exam. Each exam is graded binary: 0 or 1 point. An on-time serious attempt earns 1 point. An attempt is said to be a serious attempt when ALL questions are attempted. There is no minimum points requirement beyond making a serious attempt to earn 1 point in a short exam. To qualify for the final exam, you must earn at least 3 out of 4 points from the short exams, i.e., seriously attempt at least 3 short exams. Short exams are scheduled as follows, subject to change due to unforeseen circumstances:

• Short Exam 1: 27.10.2026, 00:01–23:59 (Moodle, online)
• Short Exam 2: 16.11.2026, 00:01–23:59 (Moodle, online)
• Short Exam 3: 14.12.2026, 00:01–23:59 (Moodle, online)
• Short Exam 4: 22.01.2027, 00:01–23:59 (Moodle, online)

The same rules apply to the short exams and the final exam: no lecture notes, books, or other helping materials are permitted. You are not allowed to get any help from anyone in any form.

Additionally, there will be:

• 4 coding exercises on data structures and algorithm implementation (all test cases provided), each worth 1 point (total: 4 points).
• 4 written homeworks, each worth 2 points, grading is ternary (0, 1, or 2 points). We check only whether each answer represents a serious attempt, not for correctness (total: 8 points).

The written final exam will be during the last lecture of the course:

• Final Exam: on Tuesday, 26.01 from 08:00 to 9:30 at Auditorium Maximum Tiefparterre Hauptgebäude Stiege 10.

Those (and only those) who qualify for the final exam but fail the course after attempting the final exam will have a chance to appear for the retake exam. Students who do not qualify for the final exam or who qualify but do not show up for the final exam receive a failing grade and are not eligible for the retake. Note that those who pass the course after the final exam cannot take the retake exam. The retake exam will be during the first week of February:

• Retake Exam: on Tuesday, 02.02 from 09:00 to 10:30 at Auditorium Maximum Tiefparterre Hauptgebäude Stiege 10.

The final/retake exam is worth 84 points, and no lecture notes, books, or other helping materials are permitted. The total score for the course is 100 points.

• 4 points for the short exams
• 4 points for the coding exercises
• 8 points for the homework sheets
• 84 points for the final/retake exam

You need to score at least 42 points out of 84 in the final/retake exam to pass the course. There is also an overall scoring requirement as mentioned below.

Minimum requirements and assessment criteria

To pass the course, students must achieve all of the following:
• At least 3 out of 4 points in the short exams (requirement to be eligible for the final/retake exam).
• At least 42 out of 84 points in the final/retake exam.
• At least 50 out of 100 points overall.

Grading scale
• Grade 1: at least 88 points (≥ 88)
• Grade 2: at least 75 points and less than 88 points (≥ 75 and < 88)
• Grade 3: at least 62 points and less than 75 points (≥ 62 and < 75)
• Grade 4: at least 50 points and less than 62 points (≥ 50 and < 62)
• Grade 5: less than 50 points (< 50)

Examination topics

All topics discussed in the course are examinable.

Reading list

Course textbook: Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, published by MIT Press, 2009 (3rd Edition in English, corresponds to 4th Edition in German, available online as pdf in Universitätsbibliothek).

Association in the course directory

Module: ADS UF-INF-09

Last modified: Tu 15.09.2026 17:06