STAT40970

Learning Outcomes:

On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory regarding all the machine learning and artificial intelligence methods introduced.
- Being able to apply a range of machine learning and artificial intelligence methods, including deep learning.
- Being able to tune and evaluate the performance of the methods introduced, benchmarking them against each other based on out-of-sample prediction performance.
- Use the statistical software R and the keras library to implement these methods.

Student Effort Hours:

Approaches to Teaching and Learning:

Requirements, Exclusions and Recommendations

- 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 "STAT30270 Statistical Machine Learning" (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 "STAT30340 Data Programming with R", 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.).

- Knowledge and understanding of basic Bayesian inference would be beneficial.

Module Requisites and Incompatibles

Assessment Strategy

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

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