Entry requirements

90 credits in information systems or the equivalent

Learning outcomes

In terms of knowledge and understanding, after completed course the student should be able to:

• understand the main AI paradigms, including Symbolic AI, Machine Learning, Deep Learning and Generative AI, and their basic principles for problem-solving and knowledge representation in AI systems,
• identify key components and how they integrate and interoperate within complex information systems,
• understand the practical considerations of implementing, deploying, and managing AI systems within an organisational IT landscape, with attention to sustainability and ethical implications.

In terms of skills and abilities, after completed course the student should be able to:

• analyse problems to systematically identify opportunities to apply appropriate AI paradigms for improving organisational processes or decision-making,
• formulate and apply data strategies for AI systems to carry out data acquisition, data management, and preprocessing of data,
• implement and integrate AI components, including developing new models, and connecting components to other parts of complex information systems,
• manage the deployment and operation of AI systems using engineering practices such as MLOps.

In terms of judgement and approach, after completed course the student should be able to:

• evaluate the suitability, limitations, and potential impact of AI technology and its applications,
• critically evaluate the ethical and sustainability implications of AI-based information systems, considering environmental, social, and economic factors,
• demonstrate an independent and critical perspective on AI technology and its applications.

Content

This course introduces students to the broad field of Artificial Intelligence (AI) and how to develop AI-based complex information systems. The main AI paradigms (Symbolic AI, Machine Learning, Deep Learning and Generative AI) are explored with a focus on the implementation of AI technology and in the practical development of AI systems. Alongside an in-depth technical exploration of AI students will explore ethical and sustainability aspects of AI. The students will develop their own AI models and integrate them into existing complex software systems. They will also develop a critical perspective on the field of AI and its related fields.

Instruction

Lectures, laboratory sessions, and supervision.

Assessment

Assignments, a written exam, and assignments.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator