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