COMPSCI 760 : Advanced Topics in Machine Learning


2023 Semester One (1233) (15 POINTS)

Course Prescription

An overview of the learning problem and the view of learning by search. Covers advanced techniques for learning such as: decision tree learning, rule learning, exhaustive learning, Bayesian learning, genetic algorithms, reinforcement learning, neural networks, explanation-based learning and inductive logic programming. Advanced experimental methods necessary for understanding machine learning research.

Course Overview

Machine learning techniques are widely used in many computing applications; for example, in web search engines, spam filtering, speech and image recognition, computer games, machine vision, credit card fraud detection, stock market analysis and product marketing applications. Machine learning implies that there is some improvement that results from the learning program having seen some data. The improvement can be in terms of some performance program (e.g., learning an expert system or improving the performance of a planning or scheduling program), in terms of finding an unknown relation in the data (e.g., data mining, pattern analysis), or in terms of customizing adaptive systems (e.g., adaptive user-interfaces or adaptive agents).
This course will introduce recent developments in the field of machine learning, and it is research-oriented. The practical component of this course involves working on a real-world research project developed with the help of the teaching team. The research project involves the definition of research questions, project planning,  data analysis workflow, programming, collaboration and regular communication of project progress in an oral or written form, including writing a literature review and a final research report. Programming skills are necessary for this course. The practical component of the course expects group work.

Course Requirements

Prerequisite: COMPSCI 361 or 762

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Discuss the idea that all machine learning algorithms have a basis and will be able to describe the basis of several algorithms (Capability 1 and 4) (Capability 1 and 4)
  2. Discuss the theory that for a particular dataset one algorithm will perform well and for another dataset a different algorithm will perform well. There is no one algorithm that performs well on all datasets. (Capability 1, 2, 3 and 5) (Capability 1, 2, 3 and 5)
  3. Independently develop and carry out to completion a research project addressing real-world problems using appropriate machine learning methodology and open-source datasets in a group of 4-5 students. (Capability 1, 2, 3, 4 and 5) (Capability 1, 2, 3, 4 and 5)
  4. Design a good set of experiments for determining the answer to some basic research question, such that they can show that the experiments actually support the question they are asking. (Capability 1, 2, 3 and 5) (Capability 1, 2, 3 and 5)


Assessment Type Percentage Classification
Final Exam 50% Individual Examination
Assignments 50% Group & Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Final Exam


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Key Topics

  • Advanced neural networks
  • Continual learning
  • Domain generalisation
  • Transfer learning
  • Data stream mining

Special Requirements

You must pass both the theory (the exam) and the practical (the research project) components to pass this course.

Workload Expectations

This course is a standard 15-point course and students are expected to spend about 150 hours in total.

Expected weekly workload: 

  • 1-hour lecture
  • 1-hour lecture review
  • 1-2 hour reading and thinking
  • 6-7 hours for the research project/ assignments
  • Students are expected to spend additional 30 hours on exam preparation

Delivery Mode

Campus Experience

This course is available for students who are remote.
Attendance is expected at scheduled activities (project presentations) to receive credit for components of the course.
Lectures will be available as recordings.
The course might include live online events including group discussions, project presentations, and lectures.
Attendance on campus is required for test/exam.
The activities for the course are scheduled as a standard weekly timetable delivery.

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.

Recommended Readings:
  • Ian Witten, Eibe Frank, Mark Hall, Christopher Pal, Data Mining -  Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2017.
  • Machine Learning Yearning, a free ebook from Andrew Ng
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
  • Zhiyuan Chen and Bing Liu. , Lifelong Machine Learning. Morgan & Claypool, August 2018.
  • Qiang Yang,  Yu Zhang, Wenyuan Dai, Transfer Learning, Cambridge University Press, 2020.
  •  Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer Machine Learning for Data Streams: with Practical Examples in MOA. MIT Press 2018

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.

The number of presentations may be reduced, and in-person discussion and feedback may be incorporated.

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 against online source material 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 31/10/2022 09:29 a.m.