Programming for Artificial Intelligence - COMP3590

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Spring Term 4 15 (7.5) Doug Santry checkmark-circle


Built on the foundation of object-oriented software development, this module provides an introduction software development for Artificial Intelligence (AI). In this module, students will gain an understanding of data analysis and statistics techniques, including summarising data, using measures of central tendency and dispersion, and effective graphical representations. Various probability models, including normal and binomial distributions, sampling and inference and predictive techniques are introduced.
Throughout the module, students will learn to embed data analysis and statistics concepts into a programming language which offers good support for AI (e.g., Python). Students will learn to use important AI-purposed libraries and tools, and apply these techniques to data loading, processing, manipulation and visualisation.


Contact hours

Total contact hours: 42 (22h lectures + 20h classes)
Private study hours: 108
Total study hours: 150

Method of assessment

This module will be assessed by 100% Coursework

Indicative reading

"Python Cookbook", David Beazley, Brian K. Jones, 3rd Edition, O'Reilly, 2013.
"Artificial Intelligence with Python", Prateek Joshi, Packt Publishing, 2017.
“Hands-on Machine Learning with Scikit-Learn and TensorFlow”, Aurélien Géron, O'Reilly, 2017.

Learning outcomes

On successfully completing the module students will be able to:
1. Understand the basics of linear algebra, probability models, including normal and binomial distributions, sampling and inference and predictive techniques;
2. Understand measures of central tendency and dispersion to summarise data;
3. Read, understand and modify small programs for data manipulation;
4. Understand the principles of data visualisation;
5. Test visualisation solutions to real data and discuss the quality of visualisation solutions through consideration of clarity and informativeness;
6. Write programs to load, manipulate, visualise and store data;
7. Use effectively a range of AI-purposed libraries, such as scientific computing library, visualisation library, data manipulation and analysis library, and machine learning library.


  1. Credit level 4. Certificate level module usually taken in the first stage of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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