Introductory description

The module will equip students with:

• A deep understanding of the main tools used in modern AI research and practice.
• The capacity to create and evaluate learning algorithms at scale, understanding abstraction levels and accuracy of the solutions provided.
• The capacity to adapt AI/ML solutions for specific problems
• An understanding of explainability, bias and transparency in learning models

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.

Mathematical introduction to learning: basics of computational learning theory, convergence guarantees.

From theory to algorithms: Clustering, Classification, Boosting, Stochastic Gradient Descent, Kernel Methods, Deep Neural Networks

Generative Models, Multimodal Foundation Models

Transformers, Attention and LLMs

In-context learning, prompt engineering

Other advanced topics in AI and ML

Throughout the module we will cover the basic elements of bias, transparency and explainability.

Learning outcomes

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

• A deep understanding of the main tools used in modern AI research and practice.
• The capacity to create and evaluate learning algorithms at scale, understanding abstraction levels and accuracy of the solutions provided.
• The capacity to adapt AI/ML solutions for specific problems.
• An understanding of explainability, bias and transparency in learning models.

Research element

Coursework will contain research element

Subject specific skills

• A deep understanding of the main tools used in modern AI research and practice.
• The capacity to create and evaluate learning algorithms at scale, understanding abstraction levels and accuracy of the solutions provided.
• The capacity to adapt AI/ML solutions for specific problems
• An understanding of explainability, bias and transparency in learning models

Transferable skills

Being able to apply deep knowledge and understanding of specialist theoretical and methodological approaches in Artificial Intelligence and Machine Learning, suggesting and incorporating interrelationships with other relevant disciplines in abstract and unpredictably complex contexts.

Students will obtain the cognitive skills to critically contribute to existing discourses and methodologies in Artificial Intelligence and Machine Learning, suggesting new ideas, and designing systematic studies in this area, based on critical analysis and evaluation.

Students will obtain practical skills in organising and communicating information, improving interpersonal, team
and networking skills through engaging in classes and computer laboratories. Formative assessment will allow students to strategically enhance their own learning.

Artificial Intelligence and Machine Learning are areas with immediate relevance for increasing ethical awareness and its practical application regarding privacy concerns and AI alignment. The associated values will help understanding the importance of personal responsibility and ethical leadership.