Official course description, subject to change:
Preliminary info last published 21/08-20

Advanced Algorithms

Course info
Language:
English
ECTS points:
15.0
Course code:
KSADALG1KU
Participants max:
12
Offered to guest students:
yes
Offered to exchange students:
-
Offered as a single subject:
yes
Price (single subject):
21250 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
MSc in Computer Science
Staff
Course semester
Semester
Efterår 2021
Start
30 August 2021
End
31 December 2021
Exam
Exam type
ordinær
Internal/External
intern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

This course will introduce students to techniques for solving complex programming tasks arising in modern IT systems. The focus in the course is on algorithm design and analysis.

Description

This course is the final course for the Algorithms specialisation. It gets students as close as possible to research in the field.

The course covers, among other topics, algorithms for numerical problems, algorithms for big data, optimization algorithms, randomized algorithms, lower bounds for computational problems, exponential-time and fixed-parameter tractable algorithms, and frameworks for parallel/distributed computation.

Formal prerequisites

As this is the final course for the Algorithms specialisation, prior knowledge of the design and analysis of data structures and algorithms (divide and conquer, greedy algorithms, dynamic programming, and basic graph algorithms) is required. This is obtained by successful completion of the preceding Master course on Algorithm Design

The course also requires familiarity with linear algebra and some experience with probability theory. This is obtained by successful completion of Linear Algebra and Probability.

Intended learning outcomes

After the course, the student should be able to:

  • Design and analyze algorithms for basic numerical problems
  • Use randomization in the design and analysis of efficient algorithms
  • Efficiently solve optimization problems using approximation algorithms
  • Solve problems on big data efficiently, also in frameworks for parallel and distributed computation
  • Identify problems that are unlikely to admit efficient algorithms and argue for their difficulty via computational complexity theory
  • Solve hard computational problems using efficient exponential-time algorithms
  • Identify parameters in computational problems that enable efficient algorithms, design and analyze parameterized algorithms
Ordinary exam
Exam type:
B: Oral exam, Internal (7-point scale)
Exam variation:
B1I: Oral exam with time for preparation. In-house.