Aims:

This module will introduce students to the fundamental concepts of machine learning, while also introducing practical tools for the application of machine learning during in classes.Learning outcomes:

On successful completion of the module, a student will be able to:

Demonstrate knowledge and an understanding of some elements of the theoretical foundations of machine learning.
	Have gained experience in building basic machine learning systems whilst understanding the limitations of such systems.
Contents:

Introduction to machine learning:

The landscape of machine learning will be introduced by application, task, and paradigm;
Supervised Learning:

Two of the primary tasks of supervised learning, Classification and Regression, will be addressed, as well as associated issues of over-fitting, and the use of regularisation and validation techniques to ameliorate this.
	In particular some of the following techniques will be examined; linear and polynomial regression, logistic regression, Naive Bayes, kernel methods, SVMs, decision trees, ensemble learning, neural networks, Gaussian processes.
Unsupervised Learning:

Two of the primary tasks of unsupervised learning, Dimensionality Reduction and Clustering, will be addressed.
	In particular some of the following techniques will be examined: PCA, manifold learning, k-means, Gaussian mixture models, EM algorithm.
Industrial applications:

Time-permitting, guest lecturers who are practitioners of machine learning in industry will discuss their experience of applying such techniques.
Requisites:

To be eligible to select this module as optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; (2) have some experience of rudimentary programming; (3) have an awareness of standard results in fundamentals of linear algebra (vectors, matrices, eigenvectors /eigenvalues etc.), elements of probability theory (random variables, expectation, variance, conditional probabilities, Bayes rule etc.), elements of statistics (sample statistics, maximum likelihood estimation etc.), and calculus (real-valued functions, derivatives, Taylor series, integrals etc.) [results from these areas will be used, without proof, throughout the module]; and (4) have also selected Robotics Systems (COMP0037).