This course provides a broad but thorough introduction to the methods and practice of statistical machine learning. Topics covered will include Bayesian inference and maximum likelihood; regression, classification, density estimation, clustering, principal and independent component analysis; parametric, semi-parametric, and non-parametric models; basis functions, neural networks, kernel methods, and graphical models; deterministic and stochastic optimisation; overfitting, regularisation, and validation.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1.  Describe a number of models for supervised, unsupervised, and reinforcement machine learning
2.  Assess the strengths and weaknesses of each of these models
3.  Interpret the mathematical equations from Linear Algebra, Statistics, and Probability Theory used in these machine learning models
4.  Implement efficient machine learning algorithms on a computer
5.  Design test procedures in order to evaluate a model
6.  Combine several models in order to gain better results
7.  Make choices for a model for new machine learning tasks based on reasoned argument