Introductory description

Applied Artificial Intelligence, the practical application of AI in business settings, largely translates to the application of deep learning architectures (neural networks), the engineering practices that support AI infrastructures (AI engineering) and the related business and strategic decisions required to specify the project. This module will bring a focus on all three of these to build upon the content of Analytics in Practice and Analytics Engineering, AI and Visualisation from term 1.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module will cover a range of topics including:

Applications of deep learning and AI
Working with unstructured data (text, images, audio, etc.)
AI development tools
Neural networks and deep learning
Computer vision and image processing
Natural language processing
Transformers and modern AI architecture
Pretraining, embedding models and finetuning
MLOps, cloud native computing and model deployment

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of neural network architectures, and determine appropriate neural network transformations and their applicability to a variety of problem settings
• Demonstrate awareness of data literacy issues and practices.
• Critically evaluate AI use-cases and plan intelligent, adaptive and ethical products;
• Critically evaluate business cases and requirements for opportinities to apply AI and deep learning concepts.

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Implement a variety of deep learning architectures;
Implement modern AI deployment patterns to provide scalability and accessibility.
Design and implement AI pipelines using modern frameworks;
Utilise MLOps deployment tools to serve AI models.

Transferable skills

Problem solving skills.
Computing skills.
Presentation skills.