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Learning outcomes for the course
On completion of the course, students will be able to:1. select and justify statistical models and methods for the data analysis of real-world problems based on reasoned argument, especially when the underlying data-generating mechanism is unknown
2. apply a range of supervised and unsupervised statistical learning algorithms to real-world problems
3. evaluate and optimise the performance of learning models and algorithms using theoretical explanation and empirical evidence, and communicate their expected accuracy and uncertainty
4. combine multiple models (e.g., through ensemble methods) to achieve higher predictive accuracy
5. conduct both theoretical and empirical comparative analyses to decide which neural network is the most suitable for a particular task
6. apply, evaluate, and optimise neural networks across a wide range of problems
7. design and implement deep learning models for real-world applications.