Aims:
Introduce the students to the basics of machine learning while giving class-based examples of applications to areas of domain specialisation.

Learning...

Machine Learning for Domain Specialists (COMP0142)

Aims:
Introduce the students to the basics of machine learning while giving class-based examples of applications to areas of domain specialisation.Learning outcomes:

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

Understand elements of the fundamental concepts and mathematical basis of machine learning; apply practical machine learning software in order to perform data analysis tasks.
Content:

General theory and mathematical foundations are presented in lectures while practical applications are presented in classes.

The module includes:

An introduction to machine learning tasks (unsupervised, supervised, reinforcement).
	Mathematical foundations (linear algebra, calculus, probability, statistics).
	Supervised Learning: including an exploration of some of the following: linear and polynomial regression, logistic regression, Naive Bayes, kernel methods, SVMs, decision trees, ensemble learning, neural networks, Gaussian processes.
	Unsupervised Learning: including an exploration of some of the following: PCA, manifold learning, k-means, Gaussian mixture models, EM algorithm.
Requisites:

To be eligible to select this module as an optional or elective, a student must: (1) be registered on a programme and year of study for which it is formally available; and (2) should have experience of rudimentary programming and 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, often without proof, throughout the module.

Self-assessment test:
Students should take the self-test available here, to assess their ability for this module.