Summer School: Engineering and Informatics

Study at the home of the very first intercontinental computer link at a university. Discover our Engineering and Informatics Summer School modules below.

Browse our modules

You can see our full list of Engineering and Informatics modules below.

Session One

26 June - 14 July 2023

• Introduction to Electronics and Robotics Engineering

  Module code: IS440

  Robotics is the engineering area that deals with the conception, design, construction, operation, application, and usage of robots. The future of robotics engineering is full of opportunities from manufacturing systems to innovative robotics for medical, military and automotive industries. Robots have a massive scope when it comes to careers. They play a crucial role in the industrial sector and help speed up manufacturing processes, such as designing bio-medical equipment. A robotics engineer core skillset is practical to aid all aspects of creating robots such as design, maintenance, testing and functioning.

  Throughout this module, you will get a strong foundation in the concepts and principles used in robotics including kinematics, tolerances, structural analysis, prototyping and novel fabrication techniques required in the design and implementation of robotic systems. Through a series of practical Workshop/ Labs, you will learn the application of computer aided design software in robotics (i.e. CAD/Solid works). 

  You will also develop skills in electronic engineering and gain knowledge and ability to program embedded systems used in robotics to interact with actuators and sensors; and acquire practical knowledge of robot integration and testing.  

  This is a hands-on, intensive project-based module that exposes you to the design and implementation of robotic mechanisms and systems from scratch. You gain further insight into the robotics and electronics taught by carrying out a series of laboratory experiments and learning how to design, build, program and test your own robotic system. 

  The module closes with a robot competition/presentation, where you as part of a team will have the opportunity to communicate and present technical information of your design and justify decisions of your robotic project to specialist and non-specialist audiences. 

  Learning outcomes:

  • Have essential knowledge and analysis of the different robotics principles, systems, system design and methods covered in the course
  • Be able to apply state-of-the-art computer aided design, novel rapid manufacturing, prototyping techniques and programming microcontrollers for the design and implementation of robotic systems.
  • Be knowledgeable and able to demonstrate the design of low-complexity embedded systems utilising the various interfacing modules of microcontrollers, sensors and software algorithmic skills required to control those systems.
  • Be a team worker - proactive, flexible, creative and trusted - with ability to problem solve, communicate technical information and justify team design decisions of a robot challenge to specialist and non-specialist audiences.

  Teaching method: Laboratory, lectures and workshops
  Assessment: 50% presentation/competition, 50% lab evaluation
  Contact hours: 43 hours
  Credits: 15 Sussex Credits
  Laboratory fees: £150
  Level: 4

Session Two

17 July – 4 August 2023

• Digital Media Concepts and Applications 
  

  Module code: IS429

  In our changing digital world, this module will provide a deeper understanding behind human perception of multimedia such as colour and sound; how perception relates to the capture, display, storage and transmission of media. This module offers the grounding into digital media of computer science and will interest students looking for an insight into digital multimedia production and distribution.

  You will be introduced to the technical principles and fundamental concepts of digital media. This will include human visual and aural perception, pixel-based and vector graphics, graphics formats, networked multimedia, web development concepts, digital video, mobile-based digital media and digital audio concepts including compression.

  You will develop core practical skills and theory, by exploring the challenges with digital video, mobile-based media, and through interactive lab sessions, you will develop your understanding of building code-driven webpages using html, CSS, JavaScript.

  This module will provide you with a theoretical understanding of the capture, display, storage and transmission of digital multimedia and introduce the technical principles and hardware underlying these concepts. You will put this theory into motion when in the labs, with access to Double Screen Computers and high-spec software to enable you to synthesise multimedia and web-based content.

  The Department of Informatics plays a major role in interdisciplinary research centres at University of Sussex, including the Centre for Computational Neuroscience and Robotics (CCNR), the Centre for Research in Cognitive Science (COGS), the Sackler Centre for Consciousness Science (SCSS) and Sussex Neuroscience.

  Learning outcomes:

  • Describe the principles behind human perception of media such as colour and sound and how perception relates to the capture, display, storage and transmission of this media.
  • Recognise the wider issues involved with multimedia production and distribution.
  • Identify and summarise the technological basis of the capture, display, storage and transmission of sound, video, image and graphical based multimedia.
  • Synthesise multimedia and web-based content.

  Teaching method: Laboratory and lectures
  Assessment: 50% coursework, 50% test
  Contact hours: 40 hours
  Credits: 15 Sussex Credits
  Level: 4

• Introduction to Electronics and Robotics Engineering
  

  Module code: IS441

  Robotics is the engineering area that deals with the conception, design, construction, operation, application, and usage of robots. The future of robotics engineering is full of opportunities from manufacturing systems to innovative robotics for medical, military and automotive industries. Robots have a massive scope when it comes to careers. They play a crucial role in the industrial sector and help speed up manufacturing processes, such as designing bio-medical equipment. A robotics engineer core skillset is practical to aid all aspects of creating robots such as design, maintenance, testing and functioning.

  Throughout this module, you will get a strong foundation in the concepts and principles used in robotics including kinematics, tolerances, structural analysis, prototyping and novel fabrication techniques required in the design and implementation of robotic systems. Through a series of practical Workshop/ Labs, you will learn the application of computer aided design software in robotics (i.e. CAD/Solid works). 

  You will also develop skills in electronic engineering and gain knowledge and ability to program embedded systems used in robotics to interact with actuators and sensors; and acquire practical knowledge of robot integration and testing.  

  This is a hands-on, intensive project-based module that exposes you to the design and implementation of robotic mechanisms and systems from scratch. You gain further insight into the robotics and electronics taught by carrying out a series of laboratory experiments and learning how to design, build, program and test your own robotic system. 

  The module closes with a robot competition/presentation, where you as part of a team will have the opportunity to communicate and present technical information of your design and justify decisions of your robotic project to specialist and non-specialist audiences. 

  Learning outcomes:

  • Have essential knowledge and analysis of the different robotics principles, systems, system design and methods covered in the course
  • Be able to apply state-of-the-art computer aided design, novel rapid manufacturing, prototyping techniques and programming microcontrollers for the design and implementation of robotic systems.
  • Be knowledgeable and able to demonstrate the design of low-complexity embedded systems utilising the various interfacing modules of microcontrollers, sensors and software algorithmic skills required to control those systems.
  • Be a team worker - proactive, flexible, creative and trusted - with ability to problem solve, communicate technical information and justify team design decisions of a robot challenge to specialist and non-specialist audiences.

  Teaching method: Laboratory, lectures and workshops
  Assessment: 50% presentation/competition, 50% lab evaluation
  Contact hours: 43 hours
  Credits: 15 Sussex Credits
  Laboratory fees: £150
  Level: 4

• Natural Language Engineering with Python
  

  Module code: IS430

  This module teaches students how a computer processes and ideally understands the English language. It explores how computers mirror human behaviour: “What we can do with the English language? How do we find certain words in documents? How do we find positive/negative words in the same document?”

  Through this module, you will acquire a deeper understanding of how large quantities of data are ranked in search engines to coincide with search terms. This module will provide you with a theoretical and practical understanding of how generic Natural Language Processing (NLP) technologies can be deployed to large quantities of realistic data. This will include technologies for text pre-processing, text classification, sequence labelling (e.g. part-of-speech tagging and named entity recognition), those that make use of manually curated linguistic resources (e.g., WordNet) and those where meaning is acquired through statistical patterns (distributional semantics). You will be introduced to the Python programming language including many of the core Data Science libraries including NumPy, SciPy, PANDAS and SCIKIT-Learn, all accessed on Double Screen Computers and high-spec software.

  Distinctive to Sussex, this module has an applied nature, with access to Double Screen Computers and high-spec software in the Laboratory, so will appeal to students looking for a more practical module. You will ideally have a background in computer science and be familiar with machine learning, and/or with some academic background in computing, engineering and business. This module explores the idea of humans versus robotics – therefore, if you are interested in Artificial Intelligence, then this module is for you.

  Learning outcomes:

  • Describe applications and summarise the underlying principles of current NLP technology.
  • Recognise the wider and ongoing challenges in using NLP technology.
  • Deploy generic NLP technologies to large quantities of realistic data.
  • Design and run an empirical investigation to determine which language processing technologies are effective in a given scenario.

  Teaching method: Lectures and seminars
  Assessment: 50% coursework, 40% test, 10% Observation
  Contact hours: 40 hours
  Credits: 15 Sussex Credits
  Level: 5

• Principles of Data Science in Python
  

  Module code: IS431

  This module will provide students with the practical tools and techniques required to build, analyse and interpret 'big data' datasets. We will cover all aspects of the Data Science process including collection, munging or wrangling, cleaning, exploratory data analysis, visualization, statistical inference, model building and implications for applications in the real world.

  We will look at data manipulating both politically and in the global business industry. We will design testable hypotheses, exploring how data is analysed in meaningful ways by applying suitable experimental methods to determine whether these hypotheses are supported by robust and reliable data. An example of the testable hypotheses that students will be asked to conclude on is: “Do male actors get paid more than females actors?” Students actively analysed medium-large datasets from IMDB statistics to prove/disprove the hypothesis.

  During the module, you will work with real world datasets and apply techniques learnt in practical sessions and lectures, to scrape data from the Internet, develop and test hypotheses and present findings. In the laboratory, students will be introduced to the Python programming language including a number of fundamental standard Python libraries/toolkits for Data Scientists including NumPy, SciPy, PANDAS and SCIKIT-Learn, all accessed on Double Screen Computers and high-spec software.

  This is an introduction course aimed for students wanting to develop a deeper understanding of Python, students should have some mathematical background and an interest in code programming.

  Learning outcomes:

  • Analyse real-world `big data’ datasets using appropriate tools and techniques.
  • Design testable hypotheses and apply suitable experimental methods to determine whether those hypotheses are supported by the data.
  • Evaluate the applicability of different tools and techniques for data analysis and visualisation in different scenarios.
  • Summarise an analysis of big data and present data in an appropriate format.

  Teaching method: Laboratory and lectures
  Assessment: 50% report, 20% presentation, 30% test
  Contact hours: 40 hours
  Credits: 15 Sussex Credits
  Level: 5

You might also be interested in:

• fees and costs
• entry requirements
• how to apply.