Official course description:
Full info last published 15/05-20

Advanced Algorithms

Course info
Language:
English
ECTS points:
15.0
Course code:
KSADALG1KU
Participants max:
25
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 manager
Assistant Professor
Course semester
Semester
Efterår 2020
Start
24 August 2020
End
22 January 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
Learning activities

We will spend 8 hours a week on lectures and exercises for the first 14 weeks of the semester.

You are expected to revise the lecture material in self-study, and you can gain more experience with algorithmic problem-solving in weekly assignment sheets that will be discussed in exercise sessions.

Mandatory activities

At least 8 mandatory take-home assignments will be given out. Assignments will be graded "approved" or "not approved". Group submissions are allowed, provided that all group members are able to present all parts of the solution.

Students need at least 7 approved assignments to be admitted to the exam. Otherwise the student will receive the grade NA (not approved) at the ordinary exam, and the student will use an exam attempt.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

Basic literature on algorithms

  • Jon Kleinberg, Éva Tardos: "Algorithm Design", 2006, Pearson

Randomized algorithms

  • Michael Mitzenmacher, Eli Upfal: "Probability and Computing: Randomized Algorithms and Probabilistic Analysis", 2nd Edition, 2017, Cambridge University Press

Approximation algorithms

  • Vijay V. Vazirani: "Approximation Algorithms", 2003, Springer

Parameterized and fine-grained complexity

  • Marek Cygan et al.: "Parameterized Algorithms", 2015, Springer

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 30%
  • Lectures: 12%
  • Exercises: 12%
  • Assignments: 30%
  • Exam with preparation: 16%
Ordinary exam
Exam type:
B: Oral exam, Internal (7-point scale)
Exam variation:
B1I: Oral exam with time for preparation. In-house.
Exam duration per student for the preparation:
30 minutes
Invigilator present for the preparation:
No
Exam duration per student for the oral exam:
30 minutes



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