COMPSCI 713 : AI Fundamentals

Science

2025 Semester One (1253) (15 POINTS)

Course Prescription

Examines the core concepts and techniques in AI, including breakthroughs in symbolic AI, machine learning, and neural networks. Real-world applications are presented, with a focus on AI research in Aotearoa/NZ and ethical considerations. The course is designed to be accessible to students with limited programming experience.

Course Overview

Examines the core concepts and techniques in AI. Students will be exposed to the various schools of thought that have shaped AI since its inception in the 1940s and the relation of these ideas to prior work. This will include an introduction to pioneering breakthroughs in symbolic AI, including heuristic search, constraint satisfaction, and knowledge representation, Bayesian inference, statistical methods of machine learning, and connectionist ideas about brain-inspired neural networks, which have given rise to the contemporary developments of deep learning. We present a range of real-world applications of AI; in particular, the course will present a catalogue characterising AI research in Aotearoa/NZ as well as technological background towards realising ethical AI. We examine both theoretical and practical components and the content is designed to be accessible to students that do not have extensive programming experience. This course is only available for MAI, PGDipAI, PGCert AI.

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. Utilise logic and symbolic representations for knowledge representation task (Capability 3, 4 and 5)
  2. Apply and explain symbolic AI methods in problem solving. (Capability 3, 4, 5 and 6)
  3. Apply and explain ML methods in classification and regression tasks. (Capability 3, 4, 5 and 6)
  4. Critically explain and discuss the relationship between symbolic and ML approaches. (Capability 3, 4, 5 and 6)
  5. Develop and evaluate deep learning solutions for simple applications. (Capability 3, 4, 5, 6, 7 and 8)
  6. Apply and explain AI techniques to a NZ context (Capability 1, 2, 5, 6, 7 and 8)

Assessments

Assessment Type Percentage Classification
Group work 40% Group Coursework
Bi-weekly quizzes 10% Individual Coursework
Presentation 10% Individual Coursework
Mid-semester Test and Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Group work
Bi-weekly quizzes
Presentation
Mid-semester Test and Final Exam
A pass mark in both the practical (Group work and presentation) and theory (Quizzes, Test and Exam)  components of the course is required to pass the course.
The Group work component will be detailled at the start of the semester and might include a group project and some group workshops. 

Key Topics

The topics covered in the course are likely to change slightly from one delivery to another, to follow trends in AI develpments. Some of the key topics covered in S1 2024 were:
  • Symbolic Logic
  • Reinforcement Learning
  • Markov Chain
  • Rule base
  • LLM based FunSearch
  • Expert Systems
  • Decision Trees
  • Soft Computing
  • Genetic Algorithms
  • Embodied AI
  • Transformer Model + DNN
  • Self-supervised learning
  • Continual learning
  • Time Series/Data Streams

Special Requirements

Attendance is expected at scheduled activities including lectures to complete components of the course.

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.

For this course, you can expect 36 hours of lectures,   60 hours of reading and thinking about the content and  54 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including lectures to complete components of the course.
Lectures will be available as recordings. Other learning activities including seminars will not be available as recordings.
Attendance on campus is required for the exam.
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.

List of readings is available on Canvas and Talis.

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.

Based on student feedback in S1 2024, we plan to implement the following changes in S1 2025:
  • The 3h of lectures per week will be organised as a 1h session, and a 2h session. This will hopefully make the in-person lecture time more engaging (including workshop style activities) and easier to attend.
  • The assessement structure was reworked to spread the assignments better over the semester (introduction of bi-weekly quizzes and a mid-semester test). 

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations 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. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

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.

Copyright

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 http://disability.auckland.ac.nz

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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

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 your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, 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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 13/11/2024 08:02 a.m.