AIML231 (2024) - Techniques in Machine Learning

Prescription

This course introduces core concepts and techniques in machine learning, as well as commonly used software libraries for implementing machine learning pipelines. It includes an overview of the machine learning field, including supervised and unsupervised learning; fundamental machine learning techniques including neural networks; tools to understand data such as exploratory data analysis, pre-processing, and visualisation; and the design machine learning pipelines. This course balances theoretical concepts of machine learning and the use of programming libraries for hands-on practice.

Course learning objectives

Students who pass this course should be able to:

1. Understand and explain concepts and terminologies related to machine learning. (BSc AI 1, 2)
2. Describe and implement simple machine learning algorithms. (BSc AI 1, 2, 3)
3. Use machine learning tools and libraries to address real-world tasks. (BSc AI 1, 2, 3, 4)
4. Design a simple machine learning pipeline for a given problem. (BSc AI 1, 2, 3, 4)

Course content

We’ve designed this course for in-person study, and to get the most of out it we strongly recommend you attend lectures on campus. Most assessment items, as well as tutorials/seminars/labs/workshops will only be available in person. Any exceptions for in-person attendance for assessment will be looked at on a case-by-case basis in exceptional circumstances, e.g., through disability services or by approval by the course coordinator.

Required Academic Background

A foundational academic background in Machine Learning and successful completion of first-year courses in programming is crucial.

Withdrawal from Course

Withdrawal dates and process:
https://www.wgtn.ac.nz/students/study/course-additions-withdrawals

Lecturers

Teaching Format

This course is offered in-person on Kelburn campus.
You will be expected to be available in-person for the final test in the assessment period.

Student feedback

This is the first time we have run the course so there is no feedback to report upon.

Dates (trimester, teaching & break dates)

• Teaching: 26 February 2024 - 31 May 2024
• Break: 01 April 2024 - 14 April 2024
• Study period: 03 June 2024 - 06 June 2024
• Exam period: 07 June 2024 - 22 June 2024

Set Texts and Recommended Readings

Required

There are no required texts for this offering.

Mandatory Course Requirements

There are no mandatory course requirements for this course.

If you believe that exceptional circumstances may prevent you from meeting the mandatory course requirements, contact the Course Coordinator for advice as soon as possible.

Assessment

Assessment Item	Due Date or Test Date	CLO(s)	Percentage
Assignment 1: Machine learning Basics and Classification	Week 5	CLO: 1,2,3	20%
Assignment 2: Data Understanding and Processing, and Regression	Week 7	CLO: 1,2,3,4	20%
Assignment 3: Clustering, Neural Network, Search and Optimisation	Week 11	CLO: 1,2,3,4	25%
Final Test	Assessment Period	CLO: 1,2,3,4	35%

Penalties

Late submissions for assignments will be managed under the "Three Late Day Policy."
 
You will have three automatic extension days, which can be applied to any assignments throughout the course. No formal application is required; instead, any remaining late hours will be automatically deducted when submitting assignments after the due date. You have the flexibility to use only a portion of their late day and retain the remainder for future use.
 
Please note that these three days are for the whole course, not for each assignment.
 
The penalty for assignments that are handed in late without prior arrangement (or use of "late days") is one grade reduction per day. Assignments that are more than one week late will not be marked.

Extensions

Individual extensions will only be granted if there are special personal circumstances, and should be negotiated with the course coordinator before the deadline. Documentation (such as medical certificate) may be requested.

Submission & Return

All work is submitted through the ECS submission system, accessible through the course web pages. Marks and comments will be returned through the ECS marking system, also available through the course web pages.

Turnitin may be used to check for plagarism in written assessment.

Workload

Although the workload will vary from week to week, you should expect to spend approximately 10–12 hours per week on the course to give a total of 150 hours study time for the course.

Teaching Plan

See: https://ecs.wgtn.ac.nz/Courses/AIML231_2024T1/LectureSchedule

Communication of Additional Information

All online material for this course can be accessed at https://ecs.wgtn.ac.nz/Courses/AIML231_2024T1/

Links to General Course Information

• Academic Integrity and Plagiarism: https://www.wgtn.ac.nz/students/study/exams/academic-integrity
• Academic Progress: https://www.wgtn.ac.nz/students/study/progress/academic-progess (including restrictions and non-engagement)
• Dates and deadlines: https://www.wgtn.ac.nz/students/study/dates
• Grades: https://www.wgtn.ac.nz/students/study/progress/grades
• Special passes: Refer to the Assessment Handbook, at https://www.wgtn.ac.nz/documents/policy/staff-policy/assessment-handbook.pdf
• Statutes and policies, e.g. Student Conduct Statute: https://www.wgtn.ac.nz/about/governance/strategy
• Student support: https://www.wgtn.ac.nz/students/support
• Students with disabilities: https://www.wgtn.ac.nz/st_services/disability/
• Student Charter: https://www.wgtn.ac.nz/learning-teaching/learning-partnerships/student-charter
• Student Feedback on University courses may be found at: http://www.cad.vuw.ac.nz/feedback/feedback_display.php
• Terms and Conditions: https://www.wgtn.ac.nz/study/apply-enrol/terms-conditions/student-contract
• Turnitin: http://www.cad.vuw.ac.nz/wiki/index.php/Turnitin
• University structure: https://www.wgtn.ac.nz/about/governance/structure
• The Use of Te Reo Māori for Assessment Policy:
  Victoria University values te reo Māori. Students who wish to submit any of their assessments in te reo Māori must refer to The Use of Te Reo Māori for Assessment Policy
  He mea nui te reo Māori ki te Whare Wānanga o te Ūpoko o te Ika. Ki te pīrangi koe ki te tuhituhi i ō aro matawai i roto i te reo Māori, tēnā me mātua whakapā atu ki te kaupapa here, The Use of Te Reo Māori for Assessment Policy
• VUWSA: http://www.vuwsa.org.nz

Offering CRN: 35049

Points: 15
Prerequisites: AIML 131 or 60 200-level points or at least a B in DATA 101; one of (COMP 103, 132)
Restrictions: COMP 307, 309, DATA 302
Duration: 26 February 2024 - 23 June 2024
Starts: Trimester 1
Campus: Kelburn