Velkommen til Københavns Universitets kursuskatalog
NDAA09023U Advanced Algorithms and
Data Structures
Volume
2014/2015  Language  English   Credit  7,5 ECTS   Level  Full Degree Master   Duration  1 block   Placement  Block 4  Schedule  A (Tues 812 + Thurs 817)   Course capacity  No limit  Continuing and further
education   Study board  Study Board of Mathematics and Computer
Science   Contracting department   Department of Computer Science
  Course responsible   Mikkel Thorup (7737a6e75787b76466a6f34717b346a71)
  Saved on the
04122014 
Education  MSc Programme in Computer
Science 
Content   Graph Algorithms such as Max Flow,
 NPcompleteness,
 Approximation Algorithms,
 Randomized Algorithms,
 Computational Geometry,
 Linear Programming and Optimization.
 Learning Outcome  Competences  Analyze computational problems in order to be able to find an
appropriate algorithmic approach to solve it.
Skills  Analyze algorithms with respect to correctness and
efficiency.
 Explain and use basic randomized algorithms.
 Recognize NPhard problems and address them, e.g., using
approximation algorithms.
 Explain and use algorithms for different abstract domains such
as graphs and geometry.
 Formulate reallife problems as algorithmic problems and solve
them.
Knowledge  Graph Algorithms such as Max Flow,
 NPcompleteness,
 Approximation Algorithms,
 Randomized Algorithms,
 Computational Geometry,
 Linear Programming and Optimization.
 Literature  See Absalon when the course is set up.  Teaching and learning methods  A mix of lectures and
exercises. 
Academic qualifications  It is assumed that the students are familiar with
basic algorithms (sorting, selection, minimum spanning trees,
shortest paths) and data structures (lists, stacks, binary trees,
search trees, heaps). 
Sign up  Self Service at KUnet

Exam  Credit  7,5 ECTS   Type of assessment  Oral examination, 30 minutes Oral exam with preparation (30 minutes) in course
curriculum.   Exam registration requirements  In order to qualify for the exam the student must
complete 2 mandatory exercises.   Aid  All aids allowed   Marking scale  7point grading scale   Censorship form  External censorship    Criteria for exam assesment  See learning outcome. 

Workload  Category  Hours  Lectures  36  Theory exercises  84  Preparation  84  Exam  2  Total  206 

Saved on the
04122014

