COMP8600
Statistical Machine Learning
- Credit
- 6 units
- Catalogue year
- 2027
- Recorded offerings
- First Semester
Course information
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; 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.
Requisite and Incompatibility
To enrol in this course you must have completed COMP6670 or COMP3670. Incompatible with COMP4670.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
Describe a number of models for supervised, unsupervised, and reinforcement machine learning
Analyse the strengths and weaknesses 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
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