Artificial Intelligence (2022)

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• Prescriptions for the PGDip(AI), BSc(Hons), MSc and MSc(Research)
• 500 Level

• Prescriptions for the PGDip(AI), BSc(Hons), MSc and MSc(Research)

  To complete a PGDip(AI), students must complete 120 points at 500 level including COMPX525 and at least another 75 points from papers listed for Artificial Intelligence.

  Enrolment in papers towards the BSc(Hons) is only by invitation of the Head of School. To complete a BSc(Hons) in Artificial Intelligence, students must complete 120 points at 500 level, including COMPX525 and at least another 75 points from papers listed for Artificial Intelligence, of which at least 30 points must be in research (normally AIMLX591).

  To complete an MSc in Artificial Intelligence, students admitted under section 2(a) of the MSc regulations must complete 180 points at 500 level including AIMLX592, COMPX525, and at least another 45 points from papers listed for Artificial Intelligence.

  To complete an MSc (Research) in Artificial Intelligence, students admitted under section 2(a) of the MSc (Research) regulations must complete 180 points at 500 level consisting of AIMLX594, COMPX525, and another 45 points from papers listed for Artificial Intelligence.

• 500 Level

  Code	Paper Title	Points	Occurrence / Location
  AIMLX591	Artificial Intelligence Dissertation	30.0	22X (Hamilton)
  A report on findings of a theoretical or empirical investigation.
  AIMLX592	Artificial Intelligence Dissertation	60.0	22X (Hamilton)
  A report on the findings of a theoretical or empirical investigation.
  AIMLX594	Artificial Intelligence Thesis	120.0	22X (Hamilton)
  An externally examined piece of written work that reports on the findings of supervised research.
  COMPX521	Machine Learning Algorithms	15.0	22A (Hamilton)
  This paper exposes students to selected machine learning algorithms and includes assignments that require the implementation of these algorithms.
  COMPX523	Data Stream Mining	15.0	22A (Hamilton)
  Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on and develops new incremental algorithms that process streams under strict resource limitations.
  COMPX525	Deep Learning	15.0	22A (Hamilton)
  This paper provides an introduction into Deep Learning, focussing on both algorithms and applications. It covers both the basics of Neural networks and current mainstream and advanced Deep Learning technology.
  COMPX529	Engineering Self-Adaptive Systems	15.0	22A (Online)
  Software-intensive systems need to manage themselves to fulfill dynamic requirements in a changing environment. Self-adaptation is employed in clouds/datacenters, digital twins, networks, IoT, autonomous robots, etc. Adaptation challenges include self-configuration, self-optimization, self-healing and self-protection.
  COMPX556	Metaheuristic Algorithms	15.0	22B (Hamilton)
  Metaheuristic are stochastic search algorithms for solving massive scale combinatorial problems where exact algorithms do not exist. This paper explores the state-of-the-art metaheuristics such as GRASP, particle swarm optimisation, and parallel metaheuristics, along with their applications in operations research, science and engin...