Recommended prerequisite for participation in the module

The module builds on knowledge acquired in the modules “Programming for Data Wrangling and Visualisation”, “Introduction to statistics”, and “Introduction to AI techniques”.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Neural network key elements and training procedures.
• Difference between predictive machine learning and generative machine learning.
• Modern deep learning architectures, such as convolutional neural networks, recurrent neural networks, autoencoders, and transformer models.
• Natural language processing, including large language models.
• Ethical challenges of modern AI systems, such as privacy, fairness and transparency.

Skills

• Usage of large language models for question-answering tasks and generative AI tasks
• Basic ethical risk assessment for AI applications.

Competences

• Design and training of deep learning models using Python frameworks like PyTorch.
• Learn deep learning models from various data types, such as text, images and relational data.

Type of instruction

Types of instruction are listed at the start of §17; Structure and contents of the programme.

Extent and expected workload

Expected module workload is 150 hours.

Exam

Exams

Name of exam	Advanced AI
Type of exam	Oral exam
ECTS	5
Permitted aids	Please see the module description in Moodle.
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures