Programming for Artificial Intelligence - COMP8270

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Autumn Term 7 15 (7.5) Fernando Otero checkmark-circle


This module covers the design and implementation of high-quality software, and provides an introduction to software development for Artificial Intelligence (AI). In this module, students will gain an understanding of data analysis and statistics techniques, including effective graphical representations.
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

Private Study: 108
Contact Hours: 42
Total: 150

Method of assessment

This module will be assessed by 100% coursework.

Indicative reading

The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.

"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

1. Read, understand, modify and evaluate small programmes for data manipulation.
2. Understand comprehensively, and evaluate critically visualisation solutions to real data and discuss the quality of visualisation solutions through consideration of clarity and informativeness.
3. Develop and evaluate critically programmes to load, manipulate, visualise and store data.
4. Master the usage and the critical and effective evaluation of a range of AI-proposed libraries, such as scientific computing library, visualisation library, data manipulation and analysis library, and machine learning library.
5. Develop non-trivial computer programmes following data analysis principles.
6. Describe concepts used in programming and to discuss programming using vocabulary from professional computer science.
7. Choose and use appropriate data structures and algorithms in the construction of programmes.
8. Apply principled design techniques in the construction of software.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  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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