- 24E-B2-2;Hold 01;;Advanced algorithms and data structures
- 24E-B2-2;Hold 02;;Advanced algorithms and data structures
- 24E-B2-2;Hold 03;;Advanced algorithms and data structures
- 24E-B2-2;Hold 04;;Advanced algorithms and data structures
- 24E-B2-2;Hold 05;;Advanced algorithms and data structures
- 24E-B2-2;Hold 06;;Advanced algorithms and data structures
- 24E-B2-2;Hold 07;;Advanced algorithms and data structures
- 24E-B2-2;Hold 08;;Advanced algorithms and data structures
NDAA09023U Advanced Algorithms and Data Structures (AADS)
MSc Programme in Computer Science
MSc Programme in Computer Science (part time)
MSc Programme in Computer Science with a minor subject
MSc Programme in Bioinformatics
Algorithms is about finding scalable solutions to computational problems, and the reliance is only increasing as we enter the world of Big Data. We want algorithms that solve problems efficiently relative to the input size. Exponential time is hopeless. We generally want polynomial time, and for large problems we need linear time. Sometimes we employ data structures that represent the input so that queries about it can be answered very efficiently. In this mandatory course, we will study the list of algorithmic topics below. Some of these topics are covered in more depth in more specialised elective courses.
Knowledge of
- Graph algorithms such as max flow.
- Data structures such as van Emde Boas Trees.
- NP-completeness.
- Exponential and parameterised algorithms for NP-hard problems.
- Approximation algorithms.
- Randomised algorithms.
- Computational geometry.
- Linear programming and optimisation.
Skills to
- Analyse algorithms with respect to correctness and efficiency.
- Explain and use basic randomised algorithms.
- Recognise 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.
Competences to
- Analyse a computational problem in order to find an appropriate algorithmic approach to solve it.
See Absalon when the course is set up.
Academic qualifications equivalent to a BSc degree is recommended.
- Category
- Hours
- Lectures
- 36
- Preparation
- 85
- Theory exercises
- 84
- Exam
- 1
- Total
- 206
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- Credit
- 7,5 ECTS
- Type of assessment
- On-site written exam, 4 hours
- Type of assessment details
- The on-site written exam is an ITX exam.
See important information about ITX-exams at Study Information, menu point: Exams -> Exam types and rules -> Written on-site exams (ITX) - Exam registration requirements
The student must get 4 out of 6 weekly assignments approved by the due date in order to qualify for the exam.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
Same as the ordinary exam.
If 10 or fewer students register for the re-exam, it will be changed to an oral examination of 25 minutes with no preparation time.
If a student is not yet qualified for the exam, they can qualify by submitting equivalent assignments. These assignments must be approved at least three weeks before the re-exam date.
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 2
- Schedule
- C
- Course capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinators
- Amir Yehudayoff (4-707c88744f73783d7a843d737a)