COMP8960 Statistical Machine Learning

Not offered in 20246 units8000-level

This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood modelling; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.

2024not offered
2025not listed
2026not listed
2027not listed

Prerequisites in 2024

The handbook's wording is ambiguous; this is a person's reading of it

All of

One of
- COMP6670Introduction to Machine Learning
- All of
  - One of
    COMP6710Structured Programming
    COMP6730Programming for Scientists
    COMP7230Introduction to Programming for Data Scientists
  - COMP8410Data Mining
  - STAT6039Principles of Mathematical Statistics
enrolment in the Master of Applied Data Analytics

Why it's read this way: The bracket closes the second pathway before "AND be enrolled", so the MADAN condition applies to both, as the wording of its successor COMP8980 confirms.

Incompatible with COMP4670, COMP8600: you can't count both.

The handbook's original wording (2024)

To enrol in this course you must have completed COMP6670 OR (6 units of (either COMP6710 or COMP6730 or COMP7230 ) and COMP8410 and STAT6039 ) AND be enrolled in the Master of Applied Data Analytics. Incompatible with COMP4670 and COMP8600 .

What it leads to in 2024

No course names COMP8960 in its 2024 prerequisites.

COMP8960 in the 2024 handbook on Programs and Courses