PERIO PERIODONTOLOGY PERIO CLINICAL CARE 4

This is a compulsory module that must be taken by student in Year 3 of the MClinDent Periodontology programme.

This module recognises the importance of...

Periodontal Clinical Care 4: Complex Multidisciplinary Care (DENT0099)

This is a compulsory module that must be taken by student in Year 3 of the MClinDent Periodontology programme.

This module recognises the importance of clinical practice in developing the skills required. The clinical care programme, which runs alongside the taught modules, develops as the student progresses through to MClinDent and specialist status, so that each module in sequence builds upon the knowledge, experience and expertise from that which has gone before.

There is teaching of graduates through treating patients in a supervised learning environment, together with seminars on multi-disciplinary treatment planning.

To continue the development of knowledge and skills required to plan and undertake treatment in a periodontal setting, building on those skills acquired in Clinical Care 3.

On successful completion of the module, students will be able to demonstrate the continuing ability to plan and deliver more complex care than in Clinical Care 3.

APPROXIMATE INFERENCE AND LEARNING|MACHINE LEARNING

Aims:

The module will present the foundations of approximate inference and learning in probabilistic graphical models (e.g. Bayesian networks and Markov...

Approximate Inference and Learning in Probabilistic Models (COMP0085)

Aims:

The module will present the foundations of approximate inference and learning in probabilistic graphical models (e.g. Bayesian networks and Markov networks), with particular focus on models composed from conditional exponential family distributions. Both stochastic (Monte Carlo) methods and deterministic approximations will be covered. The methods will be discussed in relation to practical problems in real-world inference in Machine Learning, including problems in tracking and learning.Learning outcomes:

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

Understand how to derive and implement state-of-the-art approximate inference techniques and be able to make contributions to research in this area.
Content:

Nonlinear, hierarchical (deep), and distributed models.
	Independent component analysis, Boltzmann machines, Dirichlet topic models, manifold discovery.
	Mean-field methods, variational approximations and variational Bayes.
	Expectation propagation.
	Loopy belief propagation, the Bethe free energy and extensions.
	Convex methods and convexified bounds.
	Monte-Carlo methods: including rejection and importance sampling, Gibbs, Metropolis-Hastings, anealed importance sampling, Hamiltonian Monte-Carlo, slice sampling, sequential Monte-Carlo (particle filtering).
	Other topics as time permits.
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) have taken Probabilistic and Unsupervised Learning (COMP0086).