This component assesses and encourages the assimilation of newly developed dental implant technical skills and theoretical knowledge into clinical practice. It...

This component assesses and encourages the assimilation of newly developed dental implant technical skills and theoretical knowledge into clinical practice. It has two elements, based on the clinical cases managed by the postgraduates. Each postgraduate orally presents selected cases to their peers and lecturers who are invited to critique and comment on these in a structured way. A rational approach to decision-making and treatment will be supported by clinical log book of the clinical work describing the management and outcomes of their cases

 

This module is compulsory and needs to be taken by all students completing the MSc Sports Dentistry

This module is designed to enable the postgraduate to assess and plan implant treatment in straightforward cases. This includes digital planning for guided...

This module is designed to enable the postgraduate to assess and plan implant treatment in straightforward cases. This includes digital planning for guided implant placement surgery. Rational approach to decision-making and treatment planning for replacement of missing/ failing teeth will be covered.

 

Aims:

To give an introduction to probabilistic modelling covering the broad theoretical landscape. The course aims to cover much of the first 12 chapters of...

Aims:

To give an introduction to probabilistic modelling covering the broad theoretical landscape. The course aims to cover much of the first 12 chapters of the course textbook www.cs.ucl.ac.uk/staff/d.barber/brml/ The emphasis is on probabilistic modelling of discrete variables.Learning outcomes:

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

Construct probabilistic models, learn parameters and perform inference. This forms the foundation of many models in the wider sciences and students should be able to develop novel models for applications in a variety of related areas.
Content:

Bayesian Reasoning.
	Bayesian Networks.
	Directed and Undirected Graphical Models.
	Inference in Singly-Connected Graphs.
	Hidden Markov Models.
	Junction Tree Algorithm.
	Decision Making under uncertainty.
	Markov Decision Processes.
	Learning with Missing Data.
	Approximate Inference using Sampling.
	If time permits we will also cover some deterministic approximate inference.
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; (2) have understanding of and abilities with Linear Algebra, Multivariate Calculus and Probability at mathematics FHEQ Level 4 or above; and (3) have familiarity with coding a high-level language in order to complete assessments (strongly recommend that students are skilled in Python) (some tools in Matlab and Julia are provided).