If taken as part of a BSc degree, the following courses must be passed before this course may be attempted:
- ST104a Statistics 1
- ST104b Statistics 2
- MT105a Mathematics 1 with MT105b Mathematics 2 or MT1174 Calculus.
- Linear regression and regularisation (via least squares and maximum likelihood)
- Bayesian Inference
- Resampling methods
- Non-linear models
- Tree-based methods
- Support Vector Machines
- Random forests
- Gaussian Processes
At the end of the course and having completed the essential reading and activities students should be able to:
- develop an understanding of the process to learn from data
- be familiar with a wide variety of algorithmic and model based methods to extract information from data
- apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
An individual case study piece of coursework (30%) and a 2 hr unseen written examination (70%).
The coursework will involve several computer exercises in R (no prior knowledge is required).
Rogers S. and Girolami M. A First Course in Machine Learning, Chapman & Hall/CRC Press, second edition (2011) [ISBN 9781498738484].
James G., Witten D., Hastie T. and Tibshirani R. An introduction to Statistical Learning: with Applications in R, Springer (2013) [ISBN 9781461471387]