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CS2521: ALGORITHMIC PROBLEM SOLVING (2019-2020)

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Course Overview

This course provides the knowledge needed to understand, design and compare algorithms.  By the end of the course, a student should be able to create or adapt algorithms to solve problems, determine an algorithm's efficiency, and be able to implement it. The course also introduces the student to a variety of widely used algorithms and algorithm creation techniques, applicable to a range of domains. The course will introduce students to concepts such as pseudo-code and computational complexity, and make use of proof techniques as well as the student’s programming skills.





Course Details

Study Type Undergraduate Level 2
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Nir Oren

Qualification Prerequisites

  • Either Programme Level 1 or Programme Level 2

What courses & programmes must have been taken before this course?

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

  • CS2007 Algorithmic Problem Solving (Studied)
  • CS2011 Algorithmic Problem Solving (Studied)
  • KL2576 Algorithmic Problem Solving (Studied)

Are there a limited number of places available?

No

Course Description

This course concerns seeks to equip the student with the knowledge required to identify appropriate algorithms to address computational problems. It begins by describing the concept of an algorithm, and shows how once an determine whether an algorithm solves a specific problem. Computational complexity is then introduced, allowing the student to compare algorithm efficiency. Basic data structures, such as lists, queues, trees, etc are then described and analysed. The course then examines standard approaches to algorithm creation, such as dynamic programming and search, as well as common algorithmic problems and their solutions, such as sorting.




Degree Programmes for which this Course is Prescribed

  • BSc Computing Science and Philosophy
  • Computing Science Minor
  • MSci Computing Science (AI) with Ind Pl'n

Contact Teaching Time

42 hours

This is the total time spent in lectures, tutorials and other class teaching.

Teaching Breakdown

  • 3 Lectures during University weeks 25 - 35
  • 1 Practical during University weeks 26 - 35
  • 1 Tutorial during University weeks 26 - 35

More Information about Week Numbers


Summative Assessments

Exam Type Summative Weighting 50
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Exam

Exam Type Summative Weighting 50
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Formative Assessment

There are no assessments for this course.

Resit Assessments

Resubmission of failed elements

Exam Type Summative Weighting 25
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Exam

Exam Type Summative Weighting 75
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Course Learning Outcomes

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No data available

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