School of Electronic Engineering and Computer Science

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Mr Juan Miranda Correa


Room Number: Engineering, Eng 153


Computability, Complexity and Algorithms (Undergraduate)

A theoretical course, which concerned with the theoretical core of Computer Science. The course covers some of the most successful algorithms as well as some of the most central decision problems. A large part of the course will focus on the NP versus P problem as well as other famous unsolved problem in Computer Science. To understand this problem we consider the issue of how one programming problem can be disguised as another apparently very different problem. This idea is very important in designing algorithms and plays a crucial role in the theory of NP-completeness.

Control Systems (Postgraduate)

The module provides a grounding in control systems modelling and analysis, using engineering mathematical techniques. It concludes with the examples of control systems design, underpinned by the modelling and analysis that precedes and informs the design. Syllabus: Control systems: what they are, examples of control systems, open-loop and closed-loop control systems, block diagrams of continuous (analog) and discrete-time (digital) control systems, system equations, differential equations, difference equations, linear and non-linear systems, free response, forced response, total response, steady state and transient responses, second-order systems, linearity and superposition, Laplace transform and its inverse , properties of Laplace transform, pole-zero mapping, application of Laplace transform to model systems, Routh-Hurwitz stability criterion, transfer functions and properties, analysis and design of feedback control systems, Bode analysis and design, Root-locus analysis and design, steady-state error analysis, introduction to advanced topics in control systems.

Control Systems (Undergraduate)

This module introduces the principles of control systems, particularly in respect of electronic systems. It covers: - feedback systems - modelling dynamic systems - the steady state response - the frequency response and s-plane analysis for the transient response - control of digital systems (sampled data systems) - use of the z-transform.

Data Mining (Postgraduate/Undergraduate)

Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.

Machine Learning (Postgraduate)

The aim of the module is to give students an understanding of machine learning methods, including pattern recognition, clustering and neural networks, and to allow them to apply such methods in a range of areas.


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