Artificial Intelligence (2023)

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

• Prescriptions for the GradCert(AI) and GradDip(AI)

  A Graduate Certificate and Graduate Diploma are available to graduates who have not included Artificial Intelligence at an advanced level in their first degree.

  For further details, contact the Division of Health, Engineering, Computing and Science Office.

• 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 at least 90 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 at least 90 points from the 500-level 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, and at least another 60 points from the 500-level 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, and 60 points from the 500-level papers listed for Artificial Intelligence.

• 300 Level

  Code	Paper Title	Points	Occurrence / Location
  COMPX301	Design and Analysis of Algorithms	15.0	23A (Hamilton)
  This course gives a survey of advanced algorithms and analysis of their performance, along with heuristic methods that include basic Artificial Intelligence techniques.
  COMPX307	Principles of Programming Languages	15.0	23B (Hamilton)
  The design, implementation and use of programming languages, in particular the use of functional languages to implement imperative languages will be studied. Assignments will involve challenging programming problems.
  COMPX310	Machine Learning	15.0	23B (Hamilton) & 23B (Tauranga)
  This paper introduces Machine Learning (ML) which is the science of making predictions. ML algorithms strive to be fast and highly accurate, while processing large datasets. This paper will use standard Python-based ML toolkits to teach the fundamentals of ML.
  COMPX323	Advanced Database Concepts	15.0	23A (Hamilton)
  This paper provides an introduction to the advanced features of database management systems. Students will learn to use and manipulate advanced features, and to understand and explore the technical background of large database management systems. They will have hand-on practice in using these features to create, query and maintain ...
  COMPX361	Logic and Computation	15.0	23B (Hamilton) & 23B (Tauranga)
  The syllabus includes: further development of predicate logic with application to program verification; mathematical induction including structural induction; finite state automata and regular languages; Kleene's Theorem; Turing machines, the Church-Turing thesis, universal Turing machines and the Halting problem; formal grammars a...
  COMPX367	Computational Mathematics	15.0	23B (Hamilton)
  Introduces numerical methods for solving various mathematical problems.
  DATAX322	Probability and Stochastic Processes	15.0	23A (Hamilton)
  This paper introduces students to probability theory and stochastic processes. It covers formally the theoretical foundations of probability, random variables, statistics, stochastic processes and Markov chains.

• 500 Level

  Code	Paper Title	Points	Occurrence / Location
  AIMLX591	Artificial Intelligence Dissertation	30.0	23X (Hamilton)
  A report on findings of a theoretical or empirical investigation.
  AIMLX592	Artificial Intelligence Dissertation	60.0	23X (Hamilton)
  A report on the findings of a theoretical or empirical investigation.
  AIMLX594	Artificial Intelligence Thesis	120.0	23X (Hamilton)
  An externally examined piece of written work that reports on the findings of supervised research.
  COMPX521	Machine Learning Algorithms	15.0	23B (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	23A (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	23A (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.
  COMPX546	Graph Theory	15.0	23B (Hamilton)
  An introduction to graph theory and combinatorics, including network optimisation algorithms.
  COMPX555	Bioinformatics	15.0	23B (Hamilton)
  An introduction to bioinformatics, open to students majoring in computer science or biology. It includes an overview of molecular biology, genomics, script language programming, algorithms for biological data, an introduction to machine learning and data mining, and relevant statistical methods.
  COMPX567	Advanced Computational Mathematics	15.0	23B (Hamilton)
  This paper considers computational methods for solving various mathematical problems.