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]

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