SB2b Statistical Machine Learning - 16 HT

Level: H-level
Method of Assessment: Written examination
Weight: Unit

Recommended prerequisites: Part A A9 Statistics and A8 Probability. SB2a Foundations of Statistical Inference useful by not essential.

Aims and Objectives:
Machine learning studies methods that can automatically detect patterns in data, and then use these patterns to predict future data or other outcomes of interest. It is widely used across many scientific and engineering disciplines.

This course covers statistical fundamentals of machine learning, with a focus on supervised learning and empirical risk minimisation. Both generative and discriminative learning frameworks are discussed and a variety of widely used classification algorithms are overviewed.

Synopsis
Fundamentals: Statistical learning theory, bias/variance trade-off, generalization and overfitting, regularization. Evaluating learning methods with training/test sets and cross-validation.

Supervised learning : K-nearest neighbours. Generative methods: naive Bayes, linear discriminant analysis, quadratic discriminant analysis.
Discriminative methods: logistic regression, neural networks, support vector machines, decision trees, bagging, random forests.

Reading
C. Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
T. Hastie, R. Tibshirani, J Friedman, Elements of Statistical Learning, Springer, 2009.
K. Murphy, Machine Learning: a Probabilistic Perspective, MIT Press, 2012.

Further Reading
B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996.
G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Springer, 2013.