NDAA09023U  Advanced Algorithms and Data Structures

Volume 2014/2015
Education
MSc Programme in Computer Science
Content
  • Graph Algorithms such as Max Flow,
  • NP-completeness,
  • 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 NP-hard problems and address them, e.g., using approximation algorithms.
  • Explain and use algorithms for different abstract domains such as graphs and geometry.
  • Formulate real-life problems as algorithmic problems and solve them.

Knowledge

 

 

  • Graph Algorithms such as Max Flow,
  • NP-completeness,
  • Approximation Algorithms,
  • Randomized Algorithms,
  • Computational Geometry,
  • Linear Programming and Optimization.

 

See Absalon when the course is set up.

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).
A mix of lectures and exercises.
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
7-point grading scale
Censorship form
External censorship
Criteria for exam assesment

See learning outcome.

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