NDAA09023U Advanced Algorithms and Data Structures
Volume 2013/2014
Education
MSc Programme in Computer
Science
Content
- Randomized Algorithms ,
- Graph Algorithms such as Max Flow,
- Computational Geometry,
- NP-completeness,
- Approximation Algorithms,
- Linear programming and optimization.
Learning Outcome
Competences
- Analyze computational problems in order to be able to find appropriate algorithmic approach to solve it.
Skills
- Analyze algorithms with respect to correctness and efficiency.
- Explain and use 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
- Ramdomized Algorithms,
- Computational Geometry
- Randomized Algorithms ,
- Graph Algorithms such as Max Flow,
- Computational Geometry,
- NP-completeness,
- Approximation Algorithms ,
- Linear programming and optimization,
- Analyze computational problems in order to be able to find appropriate algorithmic approach to solve it.
Skills
- Analyze algorithms with respect to correctness and efficiency.
- Explain and use 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
- Ramdomized Algorithms,
- Computational Geometry
- Randomized Algorithms ,
- Graph Algorithms such as Max Flow,
- Computational Geometry,
- NP-completeness,
- Approximation Algorithms ,
- Linear programming and optimization,
Literature
See Absalon when the course
is set up.
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).
Teaching and learning methods
A mix of lectures and
exercises.
Workload
- Category
- Hours
- Lectures
- 28
- Preparation
- 164
- Theory exercises
- 14
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 30 minutesOral 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.
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
Criteria for exam assesment
See learning outcome.
Course information
- Language
- English
- Course code
- NDAA09023U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- A
- Course capacity
- Ingen begrænsning
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Course responsibles
- Mikkel Thorup (7-6f766a7174777242666b306d7730666d)
Saved on the
17-01-2014