NATURAL LANGUAGE PROCESSING|NATURAL LANGUAGE UNDERSTANDING|STATISTICAL NATURAL LANGUAGE PROCESSING

With applications ranging from fake news detection to question answering and language translation, Natural Language Processing (NLP) is a core Artificial...

With applications ranging from fake news detection to question answering and language translation, Natural Language Processing (NLP) is a core Artificial Intelligence (AI) technology. Given the vast amounts of unstructured text data available (such as the internet) and following recent performance breakthroughs driven primarily by contextual representations and the transformer architecture, a solid understanding of NLP and its applications has become essential.

This module provides a thorough introduction to NLP ranging from an understanding of common tasks, linguistic phenomena and featurisation methods to cutting-edge transformer-based neural architectures.

This module allows the student to have a comprehensive overview on the management of eating disorders across the age-range. It covers aspects from assessment...

This course module is designed to make you think about how you would develop an idea into a business and is intended to equip you for the real world. Apart...

This course module is designed to make you think about how you would develop an idea into a business and is intended to equip you for the real world. Apart from a series of lectures that cover various important topics, much of the learning is through an experiential project in which you will work as part of a team (5-7 students). Previous projects have included proposals for taking a drug from early preclinical studies into man, or the development of a health related App. The projects are challenging in order to reflect real life.

Overview:

This module provides students with an understanding of the tools &amp; techniques required to interface between mechanical components and the wider...

Overview:

This module provides students with an understanding of the tools & techniques required to interface between mechanical components and the wider world, involving sensing, actuation (e.g. motors) modelling and control.

Topics covered:

Modelling Dynamic Systems

Time-response (“dynamics”) of first- and second-order systems: including examples of electrical filters used to reduce ripple & electrical noise.
 Ordinary differential equations (ODE), to complement (not duplicate) the Mechanics of Solids and core IEP modelling & analysis syllabus.
 Mass-spring-damper systems and the concepts of resonance.
Instrumentation

Measures of strain (strain gauge), displacement (optical encoder, LVDT, possibly hall-effect sensor), force, velocity, temperature.
Control and Electrical Power

Introduction to control. Open- and closed-loop control representation, emphasising open loop = “model-based predictive control”. Examples of regulation, including cruise control (speed), temperature control.
 Feedback, and the general structure of a control loop.
 Electric motor types, including impedance.
Learning outcomes:

Upon completion of this module students will be able to:

Create linear analytical models of dynamic systems and solve them to identify the response of those systems to a range of inputs.
 Select appropriate sensors which may be required to interface between a mechanical system and its application (e.g. displacement sensor).
 Identify the important components of a control loop and choose a general control strategy.
 Analyse a range of electrical power machines and identify their suitability for applications.