Requirements, Exclusions and Recommendations
Learning Requirements:
- Knowledge and understanding of statistical machine learning theory and methods for supervised learning and classification, at a level equivalent to that which would be achieved upon completion of "Statistical Machine Learning STAT30270" (or STAT40750), or modules with similar contents and learning outcomes.
- Knowledge of data programming and data analysis at a level equivalent to that which would be achieved upon completion of "Data Programming with R STAT40620", and modules with a relevant component of coding and implementation of statistical methods with R.
- Knowledge of linear algebra (vectors, vector spaces, matrices), calculus (derivatives), and function optimization.
- Knowledge of regression analysis and linear models, including multiple linear regression.
- Understanding of statistical inference (confidence intervals, hypothesis testing, etc.) and familiarity with standard probability distributions (Gaussian, Binomial, etc.).
Learning Recommendations:
- Knowledge and understanding of basic Bayesian inference would be beneficial.
Module Requisites and Incompatibles
Not applicable to this module.