COMPSCI 760 : Datamining and Machine Learning


2021 Semester Two (1215) (15 POINTS)

Course Prescription

An overview of the learning problem and the view of learning by search. 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. Experimental methods necessary for understanding machine learning research. Recommended preparation: COMPSCI 361 or 762

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 like research project developed with the help of the teaching team. The research project involves definition of research questions, project planning,  data analysis workflow, programming, collaboration and regular communication of project progress in a 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: Approval of the Academic Head or nominee

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
Project 40% Group Coursework
Final Exam 60% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4
Final Exam

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

Key Topics

The course will cover a selection of recent topics in machine learning, for example advanced neural networks, advanced reinforcement learning, algorithmic fairness, adversarial learning, probabilistic graphical models.

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, and 6-7 hours for the research project/ assignments.

Students are expected to spend additional 30 hours for test/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, lectures.
Attendance on campus is required for test/exam.
The activities for the course are scheduled as a standard weekly timetable delivery.

Learning Resources

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
Richard S. Sutton and Andrew G. Barto, Reinforcement Learning - An Introduction, MIT Press, 2018.
Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning,, 2018
Daphne Koller and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques

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.

Digital 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.

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.


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 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.

Level 1:  Delivered normally as specified in delivery mode
Level 2: You will not be required to attend in person.  All teaching and assessment will have a remote option.
Level 3 / 4: All teaching activities and assessments are delivered remotely

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 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 21/06/2021 12:14 p.m.