Component

MA Public Opinion and Political Behaviour
MSc Intelligent Systems and Robotics options

Year 1, Component 08

Option(s) from list
CE719-7-AU
ICT Systems Integration and Management
(15 CREDITS)
CE721-7-SP
Electronic System Design and Integration
(15 CREDITS)

This module provides first-hand experience of the design simulation and production of complex electronic circuits. A word specification is provided for a consumer electronics device for which a prototype is designed using reference and first principles. The circuit is then simulated and tested in Multisim to verify operation. Once satisfactory, a hardware prototype is developed on a prototype medium e.g. breadboard and tested in real-world conditions. Then using PCB design software, a PCB is designed and populated to produce the final product. The module has a large emphasis on the practical with a lighter emphasis on the theoretical.

CE811-7-AU
Game Artificial Intelligence
(15 CREDITS)

This module covers a range of Artificial Intelligence techniques employed in games, and teaches how games are and can be used for research in Artificial Intelligence. The module explores algorithms for creating agents that play classical board games (such as chess or checkers) and real-time games (Mario or PacMan), including single agents able to play multiple games. The course also covers Procedural Content Generation, and explores the techniques used to simulate intelligence in the latest videogames.

CE812-7-SP
Physics-Based Games
(15 CREDITS)

Many of today’s best computer games rely on realistic physics at the core of their gameplay. In this course, students are taught how these physics engines work, and how to create physics-based games of their own. Students create a physics engine from scratch, and also learn how to use existing industry-standard open-source 2-D and 3-D physics engines. The necessary principles of physics and mathematics are taught, assuming very little prior knowledge. Vectors, matrices, and numerical integration are taught on a need-to-know basis, with code examples to illustrate the methods. Each lecture is followed by a lab session, where the new techniques are programmed by each student. Almost immediately, students will create scenarios where objects are moving and bouncing around the screen realistically. Each lab session ends in creating a small physics-based game. The course is assessed through tests, and a larger game-programming assignment.

CE888-7-SP
Data Science and Decision Making
(15 CREDITS)

The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decision using data. This module blends data analysis, decision making and visualisation with practical python programming. Students will need a reasonable programming background as they will be expected to develop a complete end-to-end data science application.

CE889-7-AU
Neural Networks and Deep Learning
(15 CREDITS)

The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) in computer science and artificial intelligence. This will allow the student to build computers and intelligent machines which are able to have an artificial brain which will allow them to learn and adapt in a human like fashion.

CE889-7-SP
Neural Networks and Deep Learning
(15 CREDITS)

The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) in computer science and artificial intelligence. This will allow the student to build computers and intelligent machines which are able to have an artificial brain which will allow them to learn and adapt in a human like fashion.

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