Randomised Algorithms (RA)
Course content
Randomised algorithms are often far superior to their traditional deterministic counterparts, both in efficiency and simplicity. Many computational tasks are fundamentally impossible without randomisation. However, mastering randomised algorithms requires a basic mathematical understanding of the relevant combinatorial probability theory, and therefore a regular algorithms course will normally either skip them or teach them very superficially. Randomisation is a way of thinking, that needs a proper introduction. Applications in many areas will be considered, e.g., graph algorithms, machine learning, distributed computing, and geometry, but the focus will be on the general understanding, the goal being to give the students the foundation needed to understand and use randomisation, no matter what application area they may later be interested in.
MSc Programme in Bioinformatics
MSc Programme in Computer Science
Knowledge of
The relevant combinatorial probability theory and randomised techniques in algorithms:
- Game-Theoretic Techniques
- Moments and Deviations
- Tail Inequalities
- The Probabilistic Method
- Randomised Data Structures
- Randomised Geometric Algorithms
- Randomised Graph Algorithms
- Randomised Distributed and Parallel Algorithms
Skills in
- Proving bounds on the expected running time of randomised algorithms
- Explaining methods for bounding the probability that a random variable deviates far from its expectation
- Applying the probabilistic method to prove the existence of e.g. algorithms
- Giving simple and efficient algorithms and data structures using randomisation where more traditional deterministic approaches are more cumbersome or less efficient
Competences to
- Reason about and apply randomised techniques to computational problems that the student may later encounter in life.
Lectures and compulsory assignments.
See Absalon for a list of course literature.
The students should enjoy mathematics, as the course uses the
power of
mathematics to understand and prove good performance of algorithms.
It
is assumed that the students have completed an algorithms course
such
as Advanced Algorithms and Data Structures, and are comfortable
using
mathematical proofs in the analysis of algorithms. In addition,
some basic
knowledge of discrete probability theory is assumed, e.g. what is a
discrete
random variable, what is expectation, etc.
Academic qualifications equivalent to a BSc degree is
recommended.
This course has considerable overlap with the BSc-level course "Randomiserede algoritmer for dataanalyse". It is not recommended that you take this course if you have already taken the other. For details, contact the course organiser.
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PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 30 minutes (including grading)
- Type of assessment details
- The oral examination is with 30 minutes preparation,
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 36
- Preparation
- 85
- Theory exercises
- 84
- Exam
- 1
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAK14005U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 4
- Schedulegroup
-
B
- Capacity
- No limit
The number of seats may be reduced in the late registration period - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinator
- Jacob Holm (4-6d646b7243676c316e7831676e)
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Courseinformation of students