Universität Wien

052100 VU Algorithms and Data Structures 2 (2023W)

3.00 ECTS (2.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

NOTE:
The first class (kickoff class) will be in HS C1, UniCampus Hof 2, and not in HS 3, Währinger Straße.

Please also observe that attendance is mandatory for the first class by study law. This also applies if you are currently on the waiting list, i.e., please come to the first class if you are interested in taking this course.

  • Montag 02.10. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
    Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 09.10. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 16.10. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 23.10. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 30.10. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 06.11. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 13.11. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 13.11. 15:00 - 16:30 Seminarraum 7, Währinger Straße 29 1.OG
  • Montag 20.11. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 27.11. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 04.12. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Montag 11.12. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 08.01. 09:45 - 11:15 Digital
  • Montag 15.01. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 15.01. 16:45 - 18:15 Seminarraum 7, Währinger Straße 29 1.OG
  • Montag 22.01. 09:45 - 11:15 Hörsaal C1 UniCampus Hof 2 2G-O1-03
  • Montag 29.01. 09:45 - 11:15 Hörsaal 3, Währinger Straße 29 3.OG
    Seminarraum 5, Währinger Straße 29 1.UG
    Seminarraum 8, Währinger Straße 29 1.OG

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

The main objective of this course is to learn some of the key techniques for designing and analyzing algorithms. We will study algorithmic paradigms and show concrete examples on how to use these paradigms to solve different optimization problems. This is a theoretical course, and we’ll largely be focusing on using mathematical tools to prove the correctness and the running time complexity of the algorithms.

At the end of the course, you should be able to recognize which paradigm you would need to use for solving new problems, as well as study the correctness and the time complexity of your suggested solutions.

Prerequisites:
- Discrete Mathematics – equivalent to 051110 VO Mathematical Foundations of Computer Science 1
- Introduction to Algorithms and Data Structures – equivalent to 051024 VU Algorithms and Data Structures 1

Topics:

* Proof Techniques

* Algorithmic Strategies:
- Dynamic Programming
- Greedy Algorithms

* Data Structures and Algorithms
- Maximum Flow
- Shortest Paths
- Hashing

Art der Leistungskontrolle und erlaubte Hilfsmittel

Students can collect up to 100 regular points as follows:
- 2 online multiple choice quizzes (up to 10 points each)
- 2 homework sheets (up to 15 points each)
- scribing a lecture (up to 10 points)
- written exam (up to 40 points)

In addition, up to 20 bonus points can be collected as follows:
- attending >= 12 classes (up to 5 points)
- answer the question of the day (up to 13 x 1 points)
- intro quiz (up to 2 points)

Exams/quizzes are closed-book, no resources/help from the Internet are allowed.

Mindestanforderungen und Beurteilungsmaßstab

If P is the sum of regular and bonus points received, your grade will be:

1, if P >= 89,
2, if P >= 76,
3, if P >= 63,
4, if P >= 50,
5, otherwise.

Presence is mandatory for the first lecture. Otherwise, it is not a requirement, but strongly recommended.
There are no minimum required number of points per exam/homework sheet/quiz.

Prüfungsstoff

Everything covered in the lecture, the reading material, the homework problems, and the quizzes.

Literatur

- Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford. Introduction to Algorithms. MIT Press, 2009/2022 (3rd & 4th edition).
- Kleinberg, Jon; Tardos, Éva. Algorithm Design. Pearson, 2006.
- Erickson, Jeff. Algorithms. Online resource: http://algorithms.wtf/

Further literature will be provided via Moodle.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 05.01.2024 11:05