Module Database Search



MODULE DESCRIPTOR
Module Title
Data Structures and Algorithms for Artificial Intelligence
Reference CM1602 Version 2
Created February 2024 SCQF Level SCQF 7
Approved July 2020 SCQF Points 15
Amended April 2024 ECTS Points 7.5

Aims of Module
To provide the theory of algorithms and data structures, to evaluate their performance using complexity analysis theory and to apply algorithms and data structures to real-world problems.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Describe the fundamental concepts of algorithms and data structures.
2 Select algorithms and data structures using the theory of complexity analysis (for performance).
3 Apply appropriate data structures given real-world problem to meet requirements of programming language API's.
4 Implement algorithms and data structures to real world AI applications

Indicative Module Content
Introduction to fundamentals of algorithms. Analysis of algorithms, including asymptotic analysis, upper, lower and average boundary analysis (best, worst and average case)and order of growth classifications (constant, logarithmic, linear, linearithmic, quadratic, cubic and exponential). Basic sorting and searching algorithms. Graph algorithms, Data structures: Array, Linked Lists, Stack, Queue, Trees, Maps, Sets and Lists.

Module Delivery
The module delivery will consist of twelve weeks with each week consisting of a 2 hour lecture and a 2 hour tutorial with a total of 48 contact hours for the semester. The theoretical concepts and main principles of problem solving, algorithms and data structures will be introduced during the lectures and the students will be provided with mathematical and logical exercises to apply and test theoretical knowledge. The tutorial sessions will comprise of practical exercises to apply the theoretical principles to real-wold problems. Individual attention and evaluation will be provided to the students during the tutorial sessions.

Indicative Student Workload Full Time Part Time
Contact Hours 48 N/A
Non-Contact Hours 102 N/A
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 N/A
Actual Placement hours for professional, statutory or regulatory body    

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4
Description: Individual coursework covering all learning outcomes.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass this module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in C1.
B The student needs to achieve a B in C1.
C The student needs to achieve a C in C1.
D The student needs to achieve a D in C1.
E The student needs to achieve an E in C1.
F The student needs to achieve an F in C1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 Cormen, T. 2009. Introduction to Algorithms. 3rd edn. MIT Press.
2 Skiena, S. 2008. The Algorithm Design Manual. 2nd ed. Springer.
3 Sedgewick, R. 2011. Algorithms in Java, 4th ed. Addison-Wesley Professional.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781