DATA70132: Statistics & Machine Learning 2: AI, Complex Data, Computationally Intensive Statistics (Semester B, 2024/25 Academic Year)

Learning outcomes:

1. Define the key terms from each of the module’s five sections
2. Understand when to apply a given learning algorithm and how to judge its success, including questions of convergence and computational performance
3. Construct classifiers that capture features of already-understood data and exploit them to classify new data (supervised learning)
4. Use classification algorithms to discover and exploit previously-unknown structure in data (unsupervised learning)
5. Construct and train neural networks
6. Use MCMC methods to estimate parameters and quantify uncertainty
7. Present results, justifying choices of algorithm and communicating effectively with both technical and non-technical audiences