Mobile menu icon
Mobile menu icon Search iconSearch
Search type

United in Manchester International Summer School

Specialist subjects (morning sessions)

You will gain a solid grounding in one of four key science and engineering disciplines as part of the International Summer School.

Find out more about the streams available. You must choose only one of these streams.

Stream 1: Chemical sciences

A female student in a chemistry lab

The Chemical Sciences stream is co-organised by the School of Chemistry (CHEM), the School of Chemical Engineering and Analytical Science (CEAS) and the School of Earth and Environmental Sciences (SEES).

The stream consists of 9 morning sessions, covering 5 topics. Each session is 3 hours.

Session details

Energy quantization

Evidenced by live emission spectroscopy showing lines, (proof of quantization), also fireworks emission spectroscopy, implications of quantization: wave behaviour (strings, plates, then 3D waves: orbitals), orbital overlap, MO of O2 and N2, hetero-diatomics, S2 chemiluminescence. Follow up with some workshop questions relying on MO ideas.

  • Delivery: Demo lecture and workshop
  • Duration: 1 session
  • Lead School: CHEM

Mini-design projects to showcase the basics of Chemical Engineering

One session on mass and energy balances, one on reaction engineering, and one on how to design a reactor. Additionally, guided tour of the pilot plant at the James Chadwick building.

  • Delivery: Workshops and Pilot Plant tour
  • Duration: 3 sessions
  • Lead School: CEAS

Sustainability Challenge

Introducing you to the challenge of sustainability. You will have to work against the clock in interdisciplinary groups to develop plans for a new campus, while global responses to climate change triggered a series of ‘game changing’ interventions.

  • Delivery: Lecture/workshop
  • Duration: 1 session
  • Lead School: CEAS

Sustainable feedstocks and Industrial biotechnology

Survey of common materials currently sourced from petrochemicals. Discussion of alternative renewable feedstocks to replace petrochemical feedstocks: sugars, lignin, biomass, CO2. Apply basic chemical principles to the harvesting and manipulation on non-traditional chemical feedstocks. Sustainable catalysis and biocatalysis.

  • Delivery: Lecture/workshop + lab session
  • Duration: 2 sessions
  • Lead School: CHEM

Urban water quality management

You will learn the theory and practice of methods and approaches used by regulatory bodies, environmental consultants and research scientists to assess impacts on water quality of rivers subject to the effects of urbanisation. You will be introduced to the sources of anthropogenic stress including pollution, river re-engineering and catchment modification. You will then carry out assessments of chemical and biological water quality of urban aquatic sites in Manchester. The session will include a presentation from an environmental consultancy company on their role in water quality management and remediation.

  • Delivery: Field trip, lab analysis, lecture
  • Duration: 2 sessions
  • Lead School: SEES

Stream 2: Computer science

A male student sat looking at data graphics on a screen

The computer science stream contains the following course components:

Unit 1: Introduction to Artificial Intelligence

Unit summary

This is an introduction and short course in Artificial Intelligence (AI): It will present what is AI; the brief history of AI; and how to use AI techniques to solve basic application problems such as robot localisation. The course presents AI from a probabilistic viewpoint (the modern approach) rather than from a logic reasoning viewpoint (the traditional approach). It emphasizes using probability theory as a reasoning tool to solve AI problems with uncertainties.

Teaching formation

The teaching formation includes the lectures, example classes, and lab exercises. The lectures will present the main theoretical ideas needed to tackle AI problems; the examples classes will reinforce these through paper-and-pencil exercises, and the labs will involve the use of programs to solve them.

Learning outcomes

By the end out this course unit you will:

  • understand the overall historical development of the subject and the major research areas and the overall historical development of the subject;
  • understand the basic AI reasoning method based on probability theory;
  • be able to apply AI methods to solve some application problems as the example of localising a mobile robot.


  • Basic knowledge about probability theory.
  • Basic knowledge and experience in computer programming.

Unit 2: Introduction to Machine Learning

Unit summary

This is an introduction and short course in Machine Learning: It will present 1) what are the basic problems and concepts in Machine Learning; 2) introduce several basic machine learning methods and algorithms such as linear classification/regression, logistic regression, K-Nearest Neighbour Classifier, Support Vector Machine, Clustering algorithms; 3) how to use machine learning to solve some simple application problems.

Teaching formation

The teaching formation includes the lectures and lab exercises. The lectures will present the main theoretical ideas and algorithms needed to learn from data and solve application problems, and the labs will involve how to code and use the algorithms to solve some simple application problems.

Learning outcomes

By the end out this course unit you will:

  • understand the problems that machine learning can be used to solve;
  • understand the basic concepts of Machine Learning;
  • be able to write some very simple machine learning algorithms and apply such algorithms to solve some simple application problems.


  • Basic knowledge about university mathematics and probability theory.
  • Basic knowledge and experience in computer programming.

Stream 3: Electrical and electronic engineering

Two female students conducting research at the Photon Science Institute

During three weeks, you will take part in different learning activities, ranging from theoretical aspects of control systems to programming of robotic systems. During robotic lab sessions you will work on programming, control, navigation aspects of robotics using a miniature mobile robot, Mona, which has been developed at The University of Manchester. In addition several workshops on the design of circuits, schematics and PCBs or on mechanical CAD design with SolidWorks will be provided.

Dr Joaquin Carrasco will organise the theoretical sessions, Dr Farshad Arvin will organise the robotics sessions, and Mr Samuel Walsh will organise mechanical design and electronic circuit workshops.

Stream 4: Mathematics

Maths academic writing an equation on a chalk board

The Mathematics stream is organised by the School of Mathematics. The first half will be devoted to probability, statistics and data analysis. The second half will be a short course in coding theory.

The stream consists of 15 morning sessions cover these 2 topics. Each session is 3 hours.

Unit 1: Probability, statistics and data analysis

Today we live in an information-rich world where data plays an increasingly important role. Data analysis requires a strong background in Probability and Statistics. The aims of the course are to help students develop a knowledge of basic statistical concepts and methodology, analyse and compare statistical properties of estimators and tests, conduct exploratory data analysis and statistical inferences, and use the statistical computing software R to carry out simple data analysis.

  • Delivery: Lectures, tutorials + lab session followed by a final exam
  • Duration: 7.5 sessions

Unit 2: Coding theory

Data processing on an industrial scale, which began about 70 years ago, gave rise to the problem of guarding information from errors. Early solutions to this problem, such as the Manchester Code which is still used in TV remotes, could help spot the errors, but Richard Hamming’s code was the first to automatically correct them. Error-correcting codes allowed pictures to be transmitted from deep space using a tiny amount of power, and were then adapted to CDs, computer networks and WiFi – they are a reason why the smartphones can be so thin. Error correction, used in literally all data applications, is based on rigorous ideas and methods from pure mathematics – algebra, geometry, combinatorics, logic, which will be explored in the course and are related to cutting-edge mathematical research in group theory and representation theory.

  • Delivery: Lectures, tutorials, interactive quizzes, a short project and a formal assessment, discussion and feedback
  • Duration: 7 sessions