COMPSCI 762 : Foundations of Machine Learning
Science
2025 Semester One (1253) (15 POINTS)
Course Prescription
Course Overview
Please ignore the pre-req COMPSCI 713 and 714 for S1 2023.
Course Requirements
Capabilities Developed in this Course
| Capability 3: | Knowledge and Practice |
| Capability 4: | Critical Thinking |
| Capability 5: | Solution Seeking |
| Capability 6: | Communication |
| Capability 7: | Collaboration |
Learning Outcomes
- Explain and critique the theory that underpins machine learning (i.e. fundamental principles of learning as a computational process). This means understanding relevant concepts from both computer science and statistics (e.g, bias/variance tradeoff, minimum description length principle, kernel functions). (Capability 3, 4 and 5)
- Explain and critique the basic algorithmic principles and challenges involved in getting computers to learn from both labelled data (as in supervised learning) and unlabeled data (as in unsupervised learning), including the challenges involved in data preprocessing, model evaluation, model selection, model complexity. (Capability 3, 4 and 5)
- Compare and contrast the different machine learning techniques for supervised learning (e.g. classification and regression) and unsupervised learning (e.g. clustering) used for different dataset types. (Capability 3, 4, 5, 6 and 7)
- Develop and demonstrate a good understanding of machine learning theory by implementing machine learning algorithms from scratch, developing modifications of existing machine learning algorithms and applying existing machine learning algorithm implementations, using both core and specialized programming packages in Python. (Capability 3, 5, 6 and 7)
- Design and implement appropriate evaluation procedures in machine learning for different scenarios involving different dataset types and machine learning algorithms. (Capability 3, 5, 6 and 7)
- Develop the ability to critically interpret and evaluate the results of a machine learning algorithm run on a specific dataset, e.g, categorical, continuous, mixed-type data. (Capability 3, 4, 5, 6 and 7)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Assignments | 50% | Group & Individual Coursework |
| Final Exam | 50% | Individual Examination |
| 2 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |||||
| Assignments | ||||||||||
| Final Exam | ||||||||||
Key Topics
- Fundamentals of machine learning theory (e.g. bias/variance theory), model evaluation and validation (e.g., cross-validation, model performance measures such as accuracy, precision, recall, ROC/AUC)
- Supervised learning methods (e.g., decision trees, support vector machines, neural networks)
- Unsupervised learning methods (e.g., clustering)
Special Requirements
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:
- 3 hours of lectures
- A 1-hour tutorial
- 3 hours of reading and thinking about the content
- 3 hours of work on assignments per week
Delivery Mode
Campus Experience
Attendance is expected at scheduled activities including tutorials to complete components of the course.
Lectures will be available as recordings. Other learning activities including tutorials will be available as recordings.
The course will not include live online events.
Attendance on campus is required for the 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.
- Deisenroth, M.P., Faisal, A.A., & Ong, C.S. (2020) Mathematics for Machine Learning. Published by Cambridge University Press. Free PDF online https://mml-book.com.
- Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill.
- Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining (1st ed). Boston: Pearson Addison Wesley.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning (1st ed. 2006. Corr. 2nd printing 2011). New York, NY: Springer-Verlag New York Inc.
- Han, J., Kamber, M., & Pei, J. (2014). Data Mining (3rd Revised ed.). Morgan Kaufmann Publishers.
- Witten, I. H., Frank, E., Hall, M., & Pal, C. (2016). Data Mining (4th edition). Retrieved from https://ebookcentral.proquest.com/lib/auckland/detail.action?docID=4708912#
- Burkov, A. (2019) The Hundred-Page Machine Learning Book http://themlbook.com
- Gama, J. (2010). Knowledge discovery from data streams. Retrieved from http://marc.crcnetbase.com/isbn/9781439826126
- Bifet, A., Gavaldà, R., Holmes, G., & Pfahringer, B. (2018). Machine Learning for Data Streams with Practical Examples in MOA. Retrieved from https://mitpress.mit.edu/books/machine-learning-data-streams
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, 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.