AIML429 (2024) - Probabilistic Machine Learning

Prescription

This course teaches the ideas, algorithms and techniques of probabilistic machine learning. Topics include Bayesian inference, discriminative and generative classifiers, the EM algorithm, Gaussian processes, Markov Chain Monte Carlo, hidden Markov models, belief nets and other graphical models, and causal modelling.

Course learning objectives

Students who pass this course will be able to:

1. Apply a range of techniques for reasoning under uncertainty.
2. Apply generative models with latent variables in machine learning contexts.
3. Use probabilistic graphical models to carry out inference and learning.
4. Reason about how probabilistic machine learning could be applied in a novel context.

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.
 
If you started your programme of study remotely and can only study remotely, please contact the School so we can help and confirm what courses are available.

Required Academic Background

This course involves frequent use of mathematics and mathematical notation, and so basic mathematics (especially the basics of linear algebra and probability) is highly desirable.

Withdrawal from Course

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

Lecturers

Teaching Format

For students unable to attend in person, the critical components of learning and assessment will be available via online channels: lectures will be recorded, limited online helpdesk can be arranged, and the in-person assessments can be done via Zoom. Note that a valid reason MUST be provided for not taking part on campus and in-person.

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

Other Classes

Tutorials are currently set for Tuesdays 4-5. Venue as agreed in class.

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
A one-to-one question and answer session, allowing for guided discussion of material covered in lecture sessions. Nominally of 10 mins duration.	In the period Week 7-8, by arrangement.	CLO: 1,2,4	20%
Written Assignment 1.	Friday, Week 6	CLO: 1,2,4	25%
Written Assignment 2.	Friday, Week 12	CLO: 1,2,3,4	25%
Final test during the assessment period.	TBC During the assessment period	CLO: 1,2,3,4	30%

Penalties

Any assignment submitted after the deadline will have its maximum achievable mark reduced by 20% per day (ie. late assignments will still be marked out of 100% but the max mark capped at 80% for the first day, 60% for the second, etc). Individual extensions will only be granted in exceptional personal circumstances.

Extensions

Individual extensions will only be granted in exceptional personal circumstances, and should be negotiated with the course coordinator before the deadline whenever possible. Documentation (eg, medical certificate) may be requested.

Submission & Return

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

Workload

The student workload for this course is 150 hours.

Teaching Plan

Lecture details (notes and topics) will be available (once the course starts) on https://ecs.wgtn.ac.nz/Courses/AIML429_2024T1/

Communication of Additional Information

All online material for this course can be accessed at https://ecs.wgtn.ac.nz/Courses/AIML429_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: 33071

Points: 15
Prerequisites: AIML 420 or COMP 307; one of (MATH 177, STAT 292, 293) or approved background in Maths or Statistics;
Restrictions: COMP 421
Duration: 26 February 2024 - 23 June 2024
Starts: Trimester 1
Campus: Kelburn