MSc Computer Games Programming

This module covers the basics of multi-platform game development, using one of the most popular game engines in the world: Unity. The module also teaches the fundamentals of C#, which is used to implement everything from your game logic to character controllers.

At the end of the module, you will be able to create your very own game and publish it on different platforms. 

“Advanced Programming for Games” is the second part of this module, which can be optionally chosen if you are interested in deepening your game programming knowledge.

Linear algebra for games (vectors, matrices) with applications to 2D and 3D graphics, procedural generation of assets (L-systems), complex algebra (complex numbers, quaternions) and applications (fractals, 3D orientation).

A single term module focused on games design and how to use player behaviour to maximise acquisition/retention.

This module is taught by industry veteran Richard Leinfellner, who has more than 40 published game credits ranging from Programmer to Executive Producer.

You will study how to deconstruct designs from a designer vs a player perspective allowing you to build your own design patterns, reward cycles and hence optimise player enjoyment. We will cover how to rapidly prototype your designs using a game engine. Mobile considerations, multiplayer and the use of analytics is covered allowing the student to make informed design choices.

This one term long module (following the introduction module) is targeted directly at those who wish to work in the games industry. Technical and art positions at major or independent studios are difficult to secure, with many requiring tough tests and interviews. The approach is highly practical focusing on the key skills valued by employers in senior staff.

As well as focusing on the required programming the module also teaches the organisational skills required to work at a high level. Artists talking this module will benefit from knowing how the asset pipeline works.

A single term module focused on imparting high-quality postgraduate entrepreneurial and employment skills.

This module is led by Richard Leinfellner; an industry veteran with 30 years of experience making commercially successful games. Richard has held executive roles as Executive Producer & Vice President at Electronic Arts as well as CEO of Babel Media, he started his career as an assembly language programmer & co-founder of Palace Software in 1984.

The module focuses on the skills needed to build a viable/investment-ready business plan and pitching it to investors/employers in a industry style Dragons Den. Topics include using LEAN rapid development methodologies & tools, assessing & mitigating real risks, copyright, business structure, writing compelling CVs as well as supporting students in obtaining work placements.

Prepares students to join the industry by helping them polish their portfolio. Work is done either in small teams, on a selected individual research project, or as part of an internship/placement.

Typical examples of final projects include designing and building a level of a complete ’first playable’ prototype game (or ’the vertical slice’) for PC, or consoles or mobile platforms, of a quality to be suitable to be shown to a publisher. Alternatively, the student may choose to create a tool or process suitable for games related workflow.

The student may choose to do an internship with a games or related company. They would use this as the basis for a reflective report. When choosing this option, the student would be required to make their own arrangements to obtain such employment.

From basic design tools to conceptualising, prototyping and play testing an array of games, this module will teach you how to create compelling game mechanics within playable experiences.

You'll explore how emotion, sensory experience, interaction design, framing and purpose unfold through game design, and grapple with the tools which make play compelling. Additionally, this module looks a the different kind of possible models for play such as cooperation, skill, experience, chance, whimsy, performance, expression and simulation.

In addition to learning game design, students will learn how to talk about and understand games. From designers working with a formalised ludic approach to artists exploring liminoid spaces within play, the range of approaches will be explored.

You will leave this class with a clear understanding of the state of games and play as well as with a lexicon on how to discuss work within this space.

Modern games demand a lot in terms of complex computer-generated behaviour and content. This is a single term module that teaches you a set of core concepts that you can use to build your own state-of-the-art AI systems. It focuses on practical techniques and architectures that can be directly applied in game development: pathfinding, reactive movement, behaviour trees, HTN planning, procedural content generation. It will also give you an understanding of more advanced AI concepts that are increasingly finding their way into games, such as Monte Carlo Tree Search and techniques from Machine Learning, such as Convolutional Neural Networks (CNNs), Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs).

Interpolation and Approximation (linear, quadratic, cubic, splines, Bezier, B-splines, NURBs), Meshes (triangulations, Voronoi-Delaunay, simplifications, refinements), Light rendering, Maths for physics.

This single term module focuses on building asset pipelines for triple-A game studios and other entertainment and experience industries. Topics include game engine customisation, production of modular software components and other tools and procedural content generation. Students will learn programming practices aimed at producing efficient code, such as deterministic memory management and metaprogramming, and their specific performance implications. Through the coursework, students will acquire experience with state-of-the-art commercial environments such as Unreal Engine (with C++).

The lectures will cover the following topics:

1. Introduction to VR hardware and VR history
2. VR applications (training, education, therapy, communication, entertainment)
3. VR graphics
4. The three illusions - the Psychology behind VR
5. 3D interaction (navigation, object interaction)
6. Virtual humans: body and face animation
7. Embodiment
8. Introduction to 3D audio

The students will also gain practical experiences in the above areas with a game engine (Unity3D).

The course will introduce students to Virtual Reality and cover a selection of the topics below:

• VR history and its place in broader media art history
• Introduction to VR hardware
• Examples of VR applications across creative disciplines
• Human Perception and the Psychology behind VR
• VR graphics
• 3D interaction (navigation, object interaction)
• Narrative in VR and critical approaches to VR
• Embodiment
• Technical topics: character rigging in 3D, 3D audio etc
• Design of VR experience as part of a creative practice

Students will gain practical experiences in the above areas creating VR applications using game engines or web-based VR (example: Unity, AFrame, etc)

Students will learn advanced techniques for immersively combined real world with virtual contents including augmented reality, photogrammetry, motion capture, and computer vision. In the lab, they will be able to apply these techniques to create a mixed reality application that combines real and virtual content appropriately, and leverage the body in this experience.

Physical Computing is of increasing interest to artists, musicians, choreographers and other creative practitioners for the creation of novel artworks and also for forms of computational interaction between these objects and people.

There are many other applications of Physical Computing, for example in museums, ubiquitous and embedded computing, robotics, engineering control systems and Human-Computer Interaction. A physical environment may be sonic, tangible, tactile, visually dynamic, olfactory or any combination of these.

The module will explain and demonstrate how the environment, which is essentially continuous can be monitored by analogue electrical and mechanical sensors. Computers, however, are digital machines programmed by software. A focus of this module, therefore, is the interface between the digital and the analogue. This study encompasses basic physics, electronics, programming and software engineering. The practical objective of this module is the development of the skills needed for designing and building interactive physical devices.

The module will expose students to state-of-the-art techniques, tools, and open questions related to creative uses of data, signal processing, and machine learning. The emphasis will be on developing hands-on skills using these techniques in creative projects, and on exploring the creative potential of these techniques.

Specifically, students will learn about topics including:

• Representations and feature engineering for sensor data, audio data, image and video data, social media data, etc.
• Signal processing techniques for working effectively with this data (e.g., perceptual audio and video features, smoothing filters,
  onset detection)
• Communication protocols for working with real-time data (e.g., OpenSoundControl, Web Sockets, serial)
• Applications of classification to creative and interactive contexts: e.g., human pose recognition, activity recognition, semantic
  audio analysis
• Applications of regression to creative and interactive contexts: e.g., creating continuous gestural controllers and multimodal
  mappings (such as music visualisations, gesturally-controlled instruments)
• Applications of temporal modeling to creative and interactive contexts: e.g., gesture recognition, temporal analysis of music or
  video
• Current topics in signal processing and machine learning in music, art, and other creative industries (e.g., Google's "Deep
  Dream," chat bots, image style transfer)
• Tools for working with data, signal processing, and machine learning in creative projects, including tools for real-time data
  analysis
• Reasoning about fundamental questions in machine learning and data mining, including e.g., how can an algorithm learn from
  data? What feature representations should we use for a given problem? How do we know whether one algorithm is better than
  another?