COMPSYS 306 : Artificial Intelligence and Machine Learning

Engineering

2024 Semester Two (1245) (15 POINTS)

Course Prescription

Fundamentals of artificial intelligence, including topics from artificial neural networks, fuzzy models, genetic algorithms. Using machine learning as an application of artificial intelligence to use data for training and inference, including topics from convolutional neural networks, deep learning, pattern classification and recognition.

Course Overview

This course introduces the fundamentals and the holistic process of machine learning, including data sampling, data preparation, model training and optimisation, and model evaluation. Supervised and unsupervised machine learning techniques such as linear/logistic regression, artificial neural networks, support vector machines, and clustering algorithms will be covered in the course. The acquired knowledge of machine learning will be applied to real-world applications using modern embedded platforms with machine learning support.

Course Requirements

Prerequisite: COMPSYS 201, and COMPSYS 202 or SOFTENG 281

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand and apply machine learning concepts such as data preparation, dimensionality reduction, model training and optimisation, and model testing (Capability 3.1, 3.2 and 4.1)
  2. Understand supervised and unsupervised machine learning and some typical techniques (e.g., neural networks, support vector machine, clustering approaches) (Capability 3.1, 3.2 and 4.1)
  3. Understand the difference between classification and regression (Capability 3.1, 3.2 and 4.1)
  4. Understand how to evaluate machine learning models' performance and select the best model for an application. Besides, understanding the parameters affecting the model performance. (Capability 4.2, 5.1 and 6.1)

Assessments

Assessment Type Percentage Classification
Laboratories 40% Individual Coursework
Test 10% Individual Coursework
Project 50% Group & Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Laboratories
Test
Project

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 each week in this course, you can expect on average 3 hours of lectures, 4 hours of laboratory time, 3 hours of reading and thinking about the content and of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is required at scheduled activities including labs to complete and receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including labs will not be available as recordings.
The course will not include live online events.
Attendance on campus is required for the test, lab interviews, and project intervews.
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.

Health & Safety

Students must ensure they are familiar with their Health and Safety responsibilities, as described in the university's Health and Safety policy.
We follow the standard health and safety requirements of the Engineering MDLS labs:
https://www.auckland.ac.nz/en/engineering/about-the-faculty/facilities/health-and-safety.html

Student Feedback

At the end of every semester 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 and respond with summaries and actions.

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

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

 

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.

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 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.

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 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.