Artificial Intelligence and Machine Learning

• Credit value: 15 credits at Level 6
• Convenor: Professor George Magoulas
• Assessment: online quizzes (30% and 70%)

Using a combination of lectures and lab work, in this module we introduce you to artificial intelligence and machine learning paradigms, giving you knowledge of fundamental aspects at the theoretical and practical levels.

We will cover computational algorithms for learning from data, data-driven decision making and complex problem solving. We will also introduce you to machine learning methods such as neural networks, fuzzy logic, fuzzy clustering, bio-inspired computing, and basic concepts of feature selection and generalisation.

Indicative syllabus

• Knowledge-based systems
• Fuzzy systems
• Artificial neural networks
• Supervised and unsupervised learning
• Evolutionary computation

By the end of this module, you will be able to:

• discuss essential facts, concepts, principles and theories of artificial intelligence and machine learning methods
• recognise social, ethical and professional issues and risk involved in the design and deployment of AI and machine learning methods in applications
• describe and analyse the process of designing intelligent systems equipped with AI and machine learning components
• apply theoretical knowledge of AI and machine learning paradigms to solve classification and decision-making problems
• evaluate quality attributes and trade-offs when using AI and machine learning methods in a given problem.