Artificial Intelligence - PgCert

• About

  The Postgraduate Certificate award consists of three compulsory taught modules (totaling 60 credits). As part of the programme, students will be required to use various programming languages, including Python and R.

  Data Science and Machine Learning

  This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems.

  Knowledge Engineering

  This module will cover modern topics in a classical field of artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and their effective utilisation in e.g. decision making, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.

  Emerging and Advanced Topics in AI

  This module will cover cutting-edge topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, writing, as well as practical skills in using software to reproduce results from the literature.

  Attendance

  Typically 5-10 timetabled on campus hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course.

  Start dates

  • September 2025

  Teaching, Learning and Assessment

  Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.

  The course is assessed by 100% coursework.

  Attendance and Independent Study

  The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.

  Each course is approved by the University and meets the expectations of:

  • the relevant generic national Qualification Descriptor
  • the applicable Subject Benchmark Statement
  • the requirements of any professional, regulatory, statutory and accrediting bodies.