Statistical Machine Learning (COMP4670/COMP8600)
Undergraduate/Postgraduate level, Australian National University, 2022
Useful links:
- Progams and Courses page: COMP4691, COMP8691.
- Course website, where materials such as lecture slides can be found.
- 2021 version of the course.
- Lecture recordings on YouTube.
Overview
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:
- Describe a number of models for supervised, unsupervised, and reinforcement machine learning.
- Assess the strength and weakness of each of these models.
- Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models.
- Implement efficient machine learning algorithms on a computer.
- Design test procedures in order to evaluate a model.
- Combine several models in order to gain better results.
- Make choices for a model for new machine learning tasks based on reasoned argument.
Schedule:
| Week | Topics |
|---|---|
| 1 | Intro, scope, ML 101, probability, model selection |
| 2 | Linear regression, Bayesian linear regression |
| 3 | Linear classification, expectation maximization, mixture models |
| 4 | Generalization |
| 5 | Neural networks, linear and non-linear component analysis |
| 6 | Kernel methods, kernel machines |
| 7 | Gaussian process regression |
| 8 | Gaussian process classification |
| 9 | Graphical models |
| 10 | Graphical models, guest lecture on ML for cybersecurity |
| 11 | Sampling, ML perspectives |
| 12 | Review |