AIML429 (2021) - Probabilistic Machine Learning


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 should be able to:

  1. Use machine learning techniques that involve probabilities to solve real-world problems
  2. Use probabilistic graphical models to carry out inference and learning
  3. Apply generative models with latent variables in machine learning contexts
  4. Apply a range of techniques for reasoning under uncertainty

Required Academic Background

The course is primarily offered in-person, but there will also be a remote option and there will be online alternatives for all the components of the course for students who cannot attend in-person.
Students taking this course remotely must have access to a computer with camera and microphone and a reliable high speed internet connection that will support real-time video plus audio connections and screen sharing.  Students must be able to use Zoom; other communication applications may also be used. A mobile phone connection only is not considered sufficient.   The computer must be adequate to support the programming required by the course: almost any modern windows, macintosh, or unix laptop or desktop computer will be sufficient, but an Android or IOS tablet will not.
If the assessment of the course includes tests, the tests will generally be run in-person on the Kelburn campus. There will be a remote option for students who cannot attend in-person and who have a strong justification (for example, being enrolled from overseas). The remote test option is likely to use the ProctorU system for online supervision of the tests. ProctorU requires installation of monitoring software on your computer which also uses your camera and microphone, and monitors your test-taking in real-time. Students who will need to use the remote test option must contact the course coordinator in the first two weeks to get permission and make arrangements.
This course involves frequent use of mathematics and mathematical notation, and so confidence in basic mathematics (especially linear algebra and probability) is essential. Advanced probability theory is not required.

Withdrawal from Course

Withdrawal dates and process:


Dr Marcus Frean (Coordinator)

Teaching Format

This course will be offered in-person and online.  For students in Wellington, there will be a combination of in-person components and web/internet based resources. It will also be possible to take the course entirely online for those who cannot attend on campus, with all the components provided in-person also made available online.

Student feedback

Student feedback on University courses may be found at:

Dates (trimester, teaching & break dates)

  • Teaching: 22 February 2021 - 28 May 2021
  • Break: 05 April 2021 - 18 April 2021
  • Study period: 31 May 2021 - 03 June 2021
  • Exam period: 04 June 2021 - 19 June 2021

Class Times and Room Numbers

22 February 2021 - 04 April 2021

  • Tuesday 10:00 - 10:50 – LT105, Alan MacDiarmid Building, Kelburn
  • Thursday 10:00 - 10:50 – LT105, Alan MacDiarmid Building, Kelburn
19 April 2021 - 30 May 2021

  • Tuesday 10:00 - 10:50 – LT105, Alan MacDiarmid Building, Kelburn
  • Thursday 10:00 - 10:50 – LT105, Alan MacDiarmid Building, Kelburn


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 ItemDue Date or Test DateCLO(s)Percentage
A series of small (typically <= 1 page) exercises, on recent mathematical material, and sometimes involving minor programming tasks or a small amount of writing (30 hours total)weekly, on a day TBC.CLO: 1,2,3,420%
Assignment (25 hours)TBC in Week 6.CLO: 1,215%
Assignment (25 hours)TBC in Week 8.CLO: 1,315%
Assignment (30 hours)TBC in Week 11.CLO: 1,420%
Final test (2 hours duration)TBC during assessment period.CLO: 1,2,3,430%


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. We have a late days policy to cover minor problems.
LATE DAYS POLICY: Each student will have three "late days" which you may choose to use for any assignment or assignments during the course. There will be no penalty applied for these late days. You do not need to apply for these, instead any late days you have left will be automatically applied to assignments that you submit late.


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 required. We also (automatically) allow for 3 late days in total over the trimester for this course without penalty.

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.


The student workload for this course is 150 hours.

Teaching Plan

A detailed plan of lectures will be available (once the course starts) on

Communication of Additional Information

All online material for this course can be accessed at

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: 22 February 2021 - 20 June 2021
Starts: Trimester 1
Campus: Kelburn