263-4500-10L  Advanced Algorithms (with Project)

SemesterHerbstsemester 2018
DozierendeM. Ghaffari, A. Krause
Periodizitätjährlich wiederkehrende Veranstaltung
KommentarOnly for Data Science MSc.


263-4500-00 VAdvanced Algorithms2 Std.
Di10:15-12:00CAB G 61 »
M. Ghaffari, A. Krause
263-4500-00 UAdvanced Algorithms2 Std.
Fr10:15-12:00CAB G 59 »
M. Ghaffari, A. Krause
263-4500-10 PAdvanced Algorithms2 Std.A. Krause
263-4500-00 AAdvanced Algorithms1 Std.M. Ghaffari, A. Krause


KurzbeschreibungThis is an advanced course on the design and analysis of algorithms, covering a range of topics and techniques not studied in typical introductory courses on algorithms.
LernzielThis course is intended to familiarize students with (some of) the main tools and techniques developed over the last 15-20 years in algorithm design, which are by now among the key ingredients used in developing efficient algorithms.
Inhaltthe lectures will cover a range of topics, including the following: graph sparsifications while preserving cuts or distances, various approximation algorithms techniques and concepts, metric embeddings and probabilistic tree embeddings, online algorithms, multiplicative weight updates, streaming algorithms, sketching algorithms, and a bried glance at MapReduce algorithms.
Voraussetzungen / BesonderesThis course is designed for masters and doctoral students and it especially targets those interested in theoretical computer science, but it should also be accessible to last-year bachelor students.

Sufficient comfort with both (A) Algorithm Design & Analysis and (B) Probability & Concentrations. E.g., having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, though not required formally. If you are not sure whether you're ready for this class or not, please consulte the instructor.


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte8 KP
PrüfendeA. Krause, M. Ghaffari
RepetitionDie Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich.
Prüfungsmodusschriftlich 180 Minuten
Zusatzinformation zum PrüfungsmodusOne project (compulsory continuous performance assessment) will contribute 25% to the final grade.

Three graded exercises (compulsory continuous performance assessments) will together contribute 25% to the final grade. We will hand out three specially marked exercises, whose solutions (typeset in LaTeX or similar) are due two weeks later in each case. These three solutions will be graded and will contribute equally to the final grade.

Written exam (180 min) accounting for 50% of the final grade;
Hilfsmittel schriftlichopen book: you are permitted to consult any books, handouts, and personal notes. The use of electronic devices is not allowed.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.


Es werden nur die öffentlichen Lernmaterialien aufgeführt.


Keine Informationen zu Gruppen vorhanden.


VorrangDie Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich
Primäre ZielgruppeData Science MSc (261000)

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