Statistical Machine Learning (COMP4670/COMP8600)

Undergraduate/Postgraduate level, Australian National University, 2022

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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.

Learning Outcomes:

  1. Describe a number of models for supervised, unsupervised, and reinforcement machine learning.
  2. Assess the strength and weakness of each of these models.
  3. Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models.
  4. Implement efficient machine learning algorithms on a computer.
  5. Design test procedures in order to evaluate a model.
  6. Combine several models in order to gain better results.
  7. Make choices for a model for new machine learning tasks based on reasoned argument.


1Intro, scope, ML 101, probability, model selection
2Linear regression, Bayesian linear regression
3Linear classification, expectation maximization, mixture models
5Neural networks, linear and non-linear component analysis
6Kernel methods, kernel machines
7Gaussian process regression
8Gaussian process classification
9Graphical models
10Graphical models, guest lecture on ML for cybersecurity
11Sampling, ML perspectives