Artificial Intelligence

This module provides an introduction to some of the many Artificial Intelligence techniques which are currently, or could in the near future, be used to enhance the development of intelligent systems applied to various application domains.

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Abertay Attributes: Through engaging with this module, students will develop the Intellectual, Personal, Professional, Digital and Active Citizen Abertay Attributes.

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<ul><li>Intellectual: Students will be supported to develop skills in their chosen specialism. They will be supported to evaluate information critically and rigorously, and tackle uncertainty and information gaps with confidence and self-awareness. </li>

<li>Personal: Students will undertake activities as part of this module which will support them to be determined, ambitious, articulate, adaptable, self-reflective, resilient, practical, proactive, innovative, and enterprising. </li>

<li>Professional: Students will be supported to develop portfolios of professional development and awareness which will equip and motivate them to continue learning and professional development throughout their careers.</li>

<li>Digital: This module focuses on digital skill development as a tool for their analysis and practice. Students will be supported to develop digital fluency, knowledge, skills, and confidence to embrace digital solutions. This will prepare them for the world of work and allow them to understand the likely impact of digital technology in their chosen subject and across contexts. </li>

<li>Be active citizens: The module will encourage students to consider personality in context and support them to be inclusive, globally conscientious, and socially respectful, and self-reflective. We will maintain and continuously develop an awareness of their civic, ethical and environmental responsibilities.</li>

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By the end of this module the student should be able to:

1. Develop a critical understanding of AI techniques and technologies.
2. Evaluate the use of AI technologies and techniques in computer games.
3. Design and evaluate a suitable ML/AI solution for a data-driven problem

1 'Traditional' AI:

Rule Based Systems, Finite State Machines, Behaviour Trees.

2 Academic AI Techniques:

Fuzzy Logic and Fuzzy State Machines, Genetic Algorithms, Reinforcement Learning, Probabilistic Techniques, Artificial Neural Networks, Clustering Algorithms.

3 Applications of AI:

Combining AI techniques to produce A-life and Intelligent systems.

4 Machine Learning as a subset of AI:

The ability of a machine to learn from its environment

5 Evaluating AI/ML applications:

The range of metrics available to evaluate AI/ML performance

6 AI/ML pitfals:

Awareness of potential issues of inappropriate application of AI/ML