Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

Learning objectives

Knowledge

The student must acquire knowledge of advanced methods and theories within algorithms and data structures, including a selection of the following:

• algorithm design techniques such as greedy algorithms, dynamic programming, randomized algorithms, and linear (integer) programming 
• algorithm analysis techniques such as amortized analysis, analysis of expected complexity and experiments with algorithms
• a collection of core algorithms and data structures for solving a variety of problems from different areas of computer science: algorithms for external memory, multi-threaded algorithms, searching in text, advanced graph algorithms such as network-flow algorithms, approximate algorithms and geometric calculations

Skills

• explain the principles behind the most important algorithm design and analysis techniques
• select and apply algorithm design and analysis techniques for a given problem 
• recognize a number of problems from different computer science areas and select the most appropriate algorithms and data structures to solve them

Competences

The student, faced with a non-standard computer science problem, must be able to

• develop efficient algorithms and data structures for solving problems
• analyze the developed algorithms

Type of instruction

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams

Additional information