Image of people relaxing on Dalby Court at Imperial College London

                                                         Summer 2025

Summer School on Machine Learning, Applied Statistics, and Quantitative Finance  

             
                         Imperial College London, Department of Mathematics

                                       Applications are now open for summer 2025! 

 

We are proudly presenting our cutting-edge Summer School on Machine Learning, Applied Statistics, and Quantitative Finance! 

Designed for students who have completed at least one year of undergraduate studies in quantitative fields like Mathematics, Statistics, Computing, Physics, or Engineering. Dive into a dynamic program designed to acquaint you with diverse quantitative methods, spanning mathematics, statistics, and computing, empowering you to tackle real-world challenges in various fields. 

You will be taught by world-leading academics and researchers from Imperial's Department of Mathematics.

With three standalone week-long modules, the choice is yours. Craft your learning experience to fit your interests and schedule. Don't miss out on this opportunity to level up your skills in Machine Learning, Applied Statistics, and Quantitative Finance. Enroll now and explore a future full of exciting possibilities! 

The schedule for the Summer 2025 is as follows: 

Module 1: Introduction to Modern Machine Learning (23-27 June 2025) 

Module 2: Systematic Trading (30 June - 4 July 2025) 

Module 3: Machine Learning and Statistics for Time Series Analysis (7-11 July 2025)

The three modules are independent, stand-alone modules and you can take one, two or all three modules depending on your interests and learning goals.

 

Meet the Imperial Academics

The summer school modules will be taught by senior academics from the Department of Mathematics. Professor Johannes Muhle-Karbe and Professor Almut Veraart are the Course Directors of the Summer School Programme and the Courses will be co-delivered by the following faculty members:

Dr Ed Cohen Reader in Statistics
Dr Sarah Filippi Reader in Statistical Machine Learning
Dr Cristopher Salvi Lecturer in Mathematics and AI
Prof. Johannes Muhle-Karbe Head of Mathematical Finance, Chair in Mathematical Finance
Prof. Almut Veraart Head of the Statistics Section, Professor of Statistics
Dr Yufei Zhang Senior Lecturer in Mathematical Finance and Machine Learning

Programme Structure

Each one-week-long module consists of ca. 15 hours of lectures, workshops, tutorials and project work. 

Students will be expected to attend on the South Kensington Campus from Monday morning onwards and each module will finish on Friday afternoon. 

The programme will be taught in English.

Entry requirements

All students are expected to have completed at least one year of undergraduate studies in quantitative fields like Mathematics, Statistics, Computing, Physics, or Engineering. Prior programming experience is not required since you will be taught the relevant skills during each module.

English requirements:

All students are required to have a good command of English, and if it is not their first language, they will need to satisfy the College requirement as follows:

We ask students to bring their own laptop computer with the relevant software (R or Python - depending on the module) preinstalled. We will advise all participants prior to arrival on these requirements and will be running drop-in clinics on the first day of each module to check the preinstalled software and help with any technical issues.

Module 1: Introduction to Modern Machine Learning

Course overview

The module “Introduction to Modern Machine Learning” will be an introductory course on modern machine learning. Students will learn basic programming skills and be introduced to key ideas from machine learning, such as linear and nonlinear methods, and how to deal with overfitting. They will also explore the concepts of supervised and unsupervised learning and the basic ideas behind deep learning.   

Learning outcomes 

For delegates who have successfully completed the course, the learning outcomes will be:  

Module 2: Systematic Trading

Course overview

The module “Systematics Trading” will introduce the design and deployment of systematic trading strategies used by hedge funds and other “smart money". For these, the first key ingredient are return predictions, e.g., extracted from past prices, trades, and quotes using Machine Learning methods. The second ingredient is a price impact model that quantifies the trading costs of the strategy and allows to backtest whether “paper profits” survive in the actual trading. The course will provide a hands-on introduction to both tasks.    

Learning outcomes 

For delegates who have successfully completed the course, the learning outcomes will be:  

In the module “Machine Learning and Statistics for Time Series Analysis”, students will embark on a comprehensive exploration of probability theory and time series analysis combining ideas from modern statistics and recent advances in machine learning. They will learn how to handle trends and seasonality within time series data and develop expertise in forecasting 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.

Learning outcomes 

For delegates who have successfully completed the course, the learning outcomes will be:  

Schedule and venues

Each one-week-long module consists of ca. 15 hours of lectures, workshops, tutorials and project work. All teaching takes place on the South Kensington Campus.

The schedule for each module is as follows:

Monday

10:00 - 10:30 Registration 

10:30 - 12:00 Welcome Session and Software Check

12:00 - 13:00 Lunch Break

13:00 - 15:00 Lecture (L1, L2)

 

Tuesday

10:00 - 12:00 Lecture (L3, L4)

12:00 - 13:00 Lunch Break

13:00 - 14:30 Problem Class/Tutorial/Workshop/Project Work (P1)

 

Wednesday

10:00 - 12:00 Lecture (L5, L6)

12:00 - 13:00 Lunch Break

13:00 - 14:00 Lecture (L7)

14:30  Social activity/Trip

 

Thursday

10:00 - 12:00 Lecture (L8, L9)

12:00 - 13:00 Lunch Break

13:00 - 14:00 Lecture (L10)

14:00 - 15:30 Problem Class/Tutorial/Workshop/Project Work (P2)

 

Friday

10:00 - 12:00 Lecture (L11, L12)

12:00 - 13:00 Lunch Break

13:00 - 14:30 Wrap-up Session/Group Challenge/Social Activity

 

Please note that the schedule above is tentative and subject to minor modifications. 

Apply now!

 

Fees

The fee for each one-week-long module is £999 (Early Bird Registration Fee) if paid by 1st April 2025. 

From 2nd April 2025 onwards the registration fee for each one-week-long module is £1199.

Application process
We are running a rolling system for the admissions systems, meaning that we make decisions on a first-come, first-served basis. 
In order to apply for the summer school programme, please provide the following documents:
  • A one-page motivational letter which includes which module(s) you are applying for.
  • A CV

Please send your full application to our Admissions Team at maths.summer@imperial.ac.uk

 
Any questions?

If you have any questions about the Summer School programme or the application process, please send an email to maths.summer@imperial.ac.ukand we will get back to you.

Find out more about the Summer School  on Machine Learning, Applied Statistics, and quantitative Finance in 2025 >>

QSRI mailing list

Keep up to date with QSRI news, events and opportunities:

Sign up to our mailing list here.