COMPSCI 361 : Machine Learning


2024 Semester One (1243) (15 POINTS)

Course Prescription

Machine learning is a branch of artificial intelligence concerned with making accurate, interpretable, computationally efficient, and robust inferences from data to solve a given problem. Understand the foundations of machine learning, and introduce practical skills to solve different problems.

Course Overview

Machine learning techniques enable systems to learn from experience automatically through experience and using data. This course will introduce the field of Machine Learning, focusing on the core concepts of supervised and unsupervised learning. 
In supervised learning, we will discuss algorithms that are trained on input data labelled with the desired output.  Examples of these topics include decision trees, regression, support vector machines, and neural networks
In unsupervised learning, we aim to discover latent structure from input data where no output labels are available. Examples of these topics include clustering and association rule mining.  
Students will learn fundamental theory and algorithms that underpin these machine learning techniques, as well as develop an understanding of the relationships between these algorithms and their practical implementation. We will discuss practicalities in the application of machine learning to a range of problems.

Note: For this course you need at least programing skills in Python, basic linear algebra and basic statistical knowledge.

Course Requirements

Prerequisite: COMPSCI 220, and 15 points from DATASCI 100, STATS 101, 108, and 15 points from COMPSCI 225, MATHS 254, 255 Restriction: COMPSCI 762

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: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Explain and critique statistical principles and theories that underpin machine learning. (Capability 3, 4 and 5)
  2. Apply the fundamental principles of machine learning (ML) including data preprocessing, model selection, model complexity. (Capability 3, 4 and 5)
  3. Compare and contrast the different ML techniques, e.g., classification, regression, time-series, data stream techniques, and their application in the real world. (Capability 3, 4 and 5)
  4. Develop and apply ML programming skills using existing packages, such as Python or R, and be able to design and deploy algorithms to run ML analyses from scratch. (Capability 3, 4, 5, 6, 7 and 8)
  5. Design and implement appropriate ML evaluation procedures, e.g., cross-validation, prequential, leave-one-out, applicable to different scenarios given specific datasets. (Capability 3, 4, 5, 6, 7 and 8)
  6. Analyse and critically evaluate the results of machine learning models run on datasets from given case scenarios drawn from open repositories. (Capability 3, 4, 5, 6, 7 and 8)
  7. Consider societal issues, applications of ML in the real world and its impact. (Capability 1, 2 and 8)


Assessment Type Percentage Classification
Assignments & Tutorials 40% Group & Individual Coursework
Mid-semester Assessment 20% Individual Test
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Assignments & Tutorials
Mid-semester Assessment
Final Exam


Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at

Key Topics

  •  Fundamentals of machine learning theory, model evaluation and validation
  •  Supervised learning methods (e.g., decision trees, support vector machines, neural networks)
  •  Unsupervised learning methods (e.g., clustering, association rule 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 10 hours per week involved in each 15-point course that they are enrolled in.

For this course, each week you can expect:

  • 3 hours of lectures
  • A 1-hour tutorial
  • 2 hours of reading and thinking about the content
  • 4 hours of work on assignments and/or test preparation

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including tutorials, with submitted work to receive credit for that component of the course.
Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings.
The course will not include live online events.
Attendance on campus is required for the test/final 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.

Recommended readings (The list bellow is provided in chronological order; most of the books can be found online in pdf format. There is no single book that covers all topics. Chapters that are relevant will be indicated in class):

  • Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.
  • Han, J., Kamber, M., & Pei, J. (2014). Data Mining (3rd Revised ed.). Morgan Kaufmann Publishers. (Online via library)
  • Witten, I. H., Frank, E., Hall, M., & Pal, C. (2016). Data Mining (4th edition). Retrieved from  (Online via library)
  • Bifet, A., Gavaldà, R., Holmes, G., & Pfahringer, B. (2018). Machine Learning for Data Streams with Practical Examples in MOA. Retrieved from
  • Tan, P.-N., Steinbach, M., & Kumar, V. (2019). Introduction to data mining (2nd ed). Boston: Pearson Education Limited.
  • Burkov, A. (2019) The Hundred-Page Machine Learning Book
Additional reference:
  • Deisenroth, M.P., Faisal, A.A., & Ong, C.S. (2020) Mathematics for Machine Learning. Published by Cambridge University Press. Free PDF online
  • Hackeling, G. (2017). Mastering Machine Learning with scikit-learn. Packt Publishing Ltd. (Online via library)

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.


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 31/10/2023 10:51 a.m.