MSc Data Science (Earth and Environmental Analytics) / Course details

The module is delivered as a mixture of lectures and practical sessions and has five main sections:

1. Dimension reduction and feature extraction: principal components analysis, feature selection, information theory.
2. Classifiers and clustering: supervised and unsupervised learning, k-means and k-nearest neighbours, agglomerative clustering and dendrograms, support vector machines, linear and quadratic discriminants, Gaussian process classification, model-based clustering, mixture models and the EM algorithm.
3. Neural Networks and Deep Learning: perceptrons, back-propagation and multi-layer networks.
4. Markov-chain Monte Carlo (MCMC) methods:  Markov chains and their stationary distributions, likelihood-based inference using the Metropolis-Hastings algorithm, likelihood-free inference using Approximate Bayesian Computation, tests for convergence, applications to Bayesian inference.
5. Special Topic: Depending on the teaching staff, one special topic will be chosen to go into near-research depth, e.g. Random Forests; Social Networks; Time Series Analysis; Advanced Monte Carlo methods.

DATA70121 is a pre-requisite for DATA70132

The unit aims to introduce students to a selection of modern methods widely used in Data Science that can go beyond standard statistical frameworks. It builds on the foundation laid in Statistics and Machine Learning 1 and is strongly focussed on applications, aiming to train students to be informed users of existing algorithms.

Students should be able to:

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

The five sections of this module are essentially self-contained subunits. Each consists of a series of lectures that introduce key concepts and serve as support for practical sessions in which the students apply python-based software tools to data analysis problems.

Feedback will be made available through Turnitin

 

• Simon Rogers & Mark Girolami (2017), A First Course in Machine Learning, 2nd edition, Chapman & Hall/CRC. ISBN 9781498738484
• Christopher Bishop (2006), Pattern Recognition and Machine Learning, Springer-Verlag, New York. ISBN: 9780387310732
• S. Brooks, A. Gelman, G. Jones, and X.-L. Meng, eds. (2011), Handbook of Markov Chain Monte Carlo, Chapman and Hall/CRC. ISBN: 9781420079418
• G. James, D. Witten, T. Hastie, and R. Tibshirani (2013), An Introduction to Statistic Learning with Applications in R. Springer-Verlag, New York. ISBN 9781461471370
• Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition, Springer-Verlag. ISBN: 9780387848587