AIML420 (2023) - Artificial Intelligence


This course addresses concepts and techniques of artificial intelligence (AI). It provides a brief overview of AI history and search techniques, as well as covering important machine learning topics and algorithms with their applications, including neural networks and evolutionary algorithms. Other topics include probability and Bayesian networks, planning and scheduling. The course will also give a brief overview of a selection of other current topics in AI.

Course learning objectives

Students who pass this course will be able to:

  1. Explain fundamental concepts and techniques of artificial intelligence, particularly in areas of advanced search, machine learning, reasoning under uncertainty, planning and scheduling.
  2. Apply fundamental concepts and techniques of artificial intelligence to real world problems in regression, classification, clustering and simple planning tasks.
  3. Critically evaluate AI techniques described in AI research publications.

Course content

This course is designed for in-person study, and students are strongly recommended to attend lectures, tutorials and labs on campus. In particular, some assessment items or practical hands-on labs will require in-person attendance, although exceptions can be made under special circumstances.
Queries about any such exceptions can be sent to
Artificial Intelligence (AI) is intelligence exhibited by machines. Examples include self-driving cars, automatically planning a holiday, generating sensible conversation, learning to predict fog at Wellington Airport, reading a web page to get the answer to a question, recognising handwritten digits, detecting identity by checking fingerprints, detecting network intrusions, controlling robot actuators, processing and recognising images and signals, discovering and detecting the mathematical or logical relationship between output variables and a large number of inputs in economic and engineering tasks, or optimising parameter values in complex engineering problems. AIML 420 is an introduction to the ideas and techniques that computer scientists have developed to address these kinds of tasks.
The lectures cover following main topics: search techniques, machine learning including basic learning concepts and algorithms, neural networks and evolutionary learning, reasoning under uncertainty, planning and scheduling, knowledge based systems and AI Philosophy. The course includes a substantial amount of programming. The course will cover both science and engineering applications.

Withdrawal from Course

Withdrawal dates and process:


Dr Yi Mei

Prof Mengjie Zhang, for some tutorials and guest lectures
Dr Fangfang Zhang for some tutorials and/or guest lectures

Teaching Format

This course will be offered in-person and remotely. For students in Wellington, there will be a combination of in-person components and web/internet based resources. It will also be possible to take the course remotely for those who cannot attend on campus, with recorded lectures made available online.
During the trimester there will be typically two lectures and one tutorial per week.

Dates (trimester, teaching & break dates)

  • Teaching: 27 February 2023 - 02 June 2023
  • Break: 10 April 2023 - 23 April 2023
  • Study period: 05 June 2023 - 08 June 2023
  • Exam period: 09 June 2023 - 24 June 2023

Class Times and Room Numbers

27 February 2023 - 02 April 2023

  • Friday 11:00 - 11:50 – LT101, Maclaurin, Kelburn
27 February 2023 - 09 April 2023

  • Tuesday 11:00 - 11:50 – LT101, Maclaurin, Kelburn
  • Thursday 13:10 - 14:00 – LT205, Hugh Mackenzie, Kelburn
24 April 2023 - 04 June 2023

  • Tuesday 11:00 - 11:50 – LT101, Maclaurin, Kelburn
  • Thursday 13:10 - 14:00 – LT205, Hugh Mackenzie, Kelburn
  • Friday 11:00 - 11:50 – LT101, Maclaurin, Kelburn

Other Classes

There will be some scheduled helpdesks.


There is not required textbook for AIML 420. You can learn all the course content from the lecture notes and slides.
Some online materials are available on the course website.

A highly recommended reading is the book: Stuart J. Russell and Peter Norvig, Artificial Intelligence. A Modern Approach, Prentice-Hall, NJ (Available at the library and several bookstores and.) A lot of content of the course is from this book.
We will also provide a reading list via the course website.

Mandatory Course Requirements

There are no mandatory course requirements for this course.

If you believe that exceptional circumstances may prevent you from meeting the mandatory course requirements, contact the Course Coordinator for advice as soon as possible.


This course will be assessed through assignments and two tests. There will be four assignments, which will involve a combination of programming and discussions.
The marks and feedback will be returned in two weeks after the submission of each assignment.

Assessment ItemDue Date or Test DateCLO(s)Percentage
Assignment 1 (3-4 weeks)Week 5CLO: 1,215%
Assignment 2 (3 weeks)Week 7CLO: 1,212%
Assignment 3 (2-3 weeks)Week 10CLO: 1,210%
Assignment 4 (2 weeks)Week 12CLO: 1,28%
Research reportAssessment periodCLO: 310%
Test 1Week 8CLO: 1,210%
Test 2Assessment periodCLO: 1,235%


The penalty for assignments that are handed in late without prior arrangement is one grade reduction per day. Assignments that are more than one week late will not be marked.
There are three late days for the assignments. Students can allocate these three days among the assignments freely.


Individual extensions will only be granted in exceptional personal circumstances, and should be negotiated with the course coordinator before the deadline whenever possible. Documentation (eg, medical certificate) may be requested.

Submission & Return

All work should be submitted through the ECS submission system, accessible through the course web pages. Marks and comments will be returned through the ECS marking system, also available through the course web pages.


In order to maintain satisfactory progress in AIML 420, you should plan to spend an average of 10 hours per week on this course. A plausible and approximate breakdown for these hours would be:

  • Lectures and tutorials: 3 hours
  • Readings, revision/review, and assignments: 7 hours

If assignments are left until the last minute, the amount of work spent in particular weeks may vary greatly.

Teaching Plan

Communication of Additional Information

1. Course website:
2. Course forum
3. Email sent by the lecturers to students at their ecs email addresses.

Offering CRN: 33065

Points: 15
Prerequisites: 60 300-level CGRA, COMP, CYBR, DATA, SWEN or NWEN pts
Restrictions: COMP 307, COMP 420
Duration: 27 February 2023 - 25 June 2023
Starts: Trimester 1
Campus: Kelburn