Machine learning
This course covers a wider range of such model based and algorithmic machine learning methods, illustrated in various real-world applications and datasets. At the same time, the theoretical foundation of the methodology is presented is some cases.
- Linear regression and regularisation (via least squares and maximum likelihood)
- Bayesian Inference
- Classification
- Resampling methods
- Clustering
- Non-linear models
- Tree-based methods
- Support Vector Machines
- Random forests
- Gaussian Processes
If you complete the course successfully, you 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.
- 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]
