COMPSCI 714 : AI Architecture and Design


2024 Semester One (1243) (15 POINTS)

Course Prescription

Equips students with the ability to develop AI applications by introducing well-established AI frameworks and using web-based interactive computing platforms. Students will acquire the skills to implement simple AI techniques using these frameworks and evaluate their performance. Introduces basic practical technologies to investigate artificial intelligence techniques.

Course Overview

In this course, students will gain knowledge of the steps involved in designing, training and evaluating an AI system. The course contains a large amount of pratical work, with regular tutorials and a group project. Students will use well-known frameworks to implement AI applications, mainly based on deep neural networks. 
Students will learn how to build AI systems to solve problems using different types of input data. They will follow a pipeline including pre-processing data, choosing an adapted model architecture, making important design choices, tuning hyper-parameters, and evaluating the solution. 
Students will also be introduced to additional important topics such as transfer learning and generative AI systems.
We want this course to give students relevant practical knowledge and skills to develop their career in AI research or industry. To that end, student will also attend guest lectures featuring industry and research speakers. 

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: People and Place
Capability 2: Sustainability
Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Capability 7: Collaboration
Capability 8: Ethics and Professionalism
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Critically evaluate the importance and apply data pre-processing and parsing for machine learning training, as well as identify potential issues of bias and its ethical implications. (Capability 1, 3, 4, 5 and 8)
  2. Apply and configure an AI environment using well-established AI frameworks and using web-based interactive computing platforms. (Capability 3, 4 and 5)
  3. Develop, create, and evaluate AI models and identify guidelines for using them. (Capability 1, 2, 3, 4, 5, 6 and 8)
  4. Apply and evaluate real-world datasets and implement an AI model in Python. (Capability 3, 5, 7 and 8)


Assessment Type Percentage Classification
Tutorial assignments 50% Individual Coursework
Group project 20% Group Coursework
Test 30% Individual Test
Assessment Type Learning Outcome Addressed
1 2 3 4
Tutorial assignments
Group project

A pass mark in both the practical (assignments and project) and theory (test) components of the course is required to pass the course.


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Key Topics

Some of the key topics taught in this course are:

  • Data pre-processing
  • AI pipeline
  • Basics of neural networks and Deep Neural Networks (DNN) implementation
  • DNN architectures to learn with images and sequences
  • DNN design choices
  • Hyperparameter tuning
  • Transfer learning
  • Generative AI architectures
  • Cloud computing
  • Additional topics relevant to AI architecture and design

Workload Expectations

This course is a standard 15-point course and students are expected to spend 10 hours per week involved in each 15-point course that they are enrolled in.

In total, expect 150 hours of work over the 12 weeks, broken down into as follows: 36 hours of lectures/tutorials, 48 hours of reading and thinking about the content and 66 hours of work on assignments and/or test preparation.

You can expect a typical week of work to be broken down as follows:

  • 3 hours of lecture/tutorial (tutorials will be run at lecture times)
  • 4 hours of reading and thinking about the content 
  • 5.5 hours of work on assignments/projects and/or test preparation

Delivery Mode

Campus Experience

Attendance is required at scheduled activities including lectures and tutorials to complete components of the course.
Lectures and tutorials will be available as recordings, but attentance is crutial for you to complete the course. 
Attendance on campus is required for the test.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

You are strongly recommended to bring a personal laptop in tutorials. 
If you do not own one, laptop loan options are available at the University of Auckland Student IT Hub:

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

This course is run for the first time in S1 2024. We will assess strengths and potential improvements of the initial design through student feedback. 

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 06/11/2023 08:36 a.m.