Gain data analytics skills and apply them to challenging problems in finance and bioimaging

Machine Learning & Applied Statistics Summer School will introduce you to a range of quantitative methods from mathematics, statistics and computing and will enable you to use these methods in applications in various fields including finance and bioimaging.

This course is offered jointly by the Department of Mathematics and Imperial College Business School and facilitated by the Quantitative Sciences Research Institute. You will be taught by faculty from the Department of Mathematics.

By the end of this machine learning summer course, you will:

  • Understand a range of statistical and mathematical techniques to manipulate empirical data sets

  • Implement machine learning algorithms

  • Explain time series modelling

  • Understand spatial data modelling

  • Apply learnt techniques to real life data sets

Course content

Week one

In the first week, students will learn basic probability theory and basic programming skills. They will then study the key ideas of time series analysis including how to deal with trend and seasonality in data and how to forecast in linear time series models. The theoretical developments will be applied to various data sets with a particular focus on financial data. Students will also learn how risk measures such as value-at-risk and expected shortfall can be computed.

Week two

In the second week, students will familiarise themselves with handling spatial data. The quantitative methods taught in this part of the course are motivated from applications in the life sciences, more precisely from bioimaging. Bioimaging methods aim to observe biological processes at cellular and sub-cellular level. It is a fundamental tool of the life sciences and has led to some of the most important advances in modern medicine. Students will explore some statistical methods that can be used for analysing and interpreting spatial data extracted from bioimages.

Week three

In the third week, students will be introduced to key ideas from machine learning, such as linear and nonlinear methods, how to deal with the problem of overfitting. They will also explore the concepts of supervised and unsupervised learning as well as the basic ideas behind deep learning. 

Teaching methods

The course will be delivered by a mix of face-to-face lectures and classes. Lecture content and class material will be made available through an interactive online teaching and learning hub – The Summer School Hub.
Case studies, in-class computing demonstrations and exercises will be used to link the theoretical concepts you learn to applications. You will also be expected to complete significant private study and exam preparation outside of your scheduled classes.



Academic level: Equivalent to an undergraduate course

Entry requirement: A level mathematics (grade A or above) or equivalent. It is desirable (but not a formal requirement) that students have some basic programming skills. Designed for students who have successfully completed at least one year of undergraduate studies in a quantitative subject such as Mathematics, Statistics, Computing, Physics, or Engineering. 

Suggested credit level: 3 – 4 US / 7.5 ECTS credits. Your home institution will determine how much credit is awarded

For more details view our entry requirements.


  • One individual examination after the first week – (33.33% of final mark)

  • One individual final examination at the end of the third week – (66.66% of final mark)

Imperial College London will issue an official transcript with a final overall numerical mark – a breakdown of results will not be provided.

Imperial College London reserves the right to change or alter the courses offered without notice.

Summer School 2021 

Courses for 2021 are to be confirmed, and applications will open in early 2021.

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The Applied Statistics & Machine Learning Summer School is an exciting addition to our Summer School courses. It introduces students to a wide range of quantitative techniques for analysing empirical data from various application areas, such as finance and bioimaging. The teaching will be organised around selected case studies where students will gain insights into modern tools from statistics and machine learning. The course is relevant for students who are interested in extending their quantitative skills and might want to pursue a career in data science.
Almut Veraart
Course Director
Almut Veraart