Summer 2024
Summer School on Machine Learning, Applied Statistics, and Quantitative Finance
Imperial College London, Department of Mathematics
Introducing 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 2024 is as follows:
Module 1: Systematic Trading (24-28 June 2024)
Module 2: Introduction to Modern Machine Learning (1-5 July 2024)
Module 3: Machine Learning and Statistics for Time Series Analysis (8-12 July 2024)
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:
Reader in Statistics
Reader in Statistical Machine Learning
Senior Lecturer in Machine Learning and Mathematical Finance
Head of Mathematical Finance, Chair in Mathematical Finance
Head of the Statistics Section, Professor of Statistics
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:
a minimum score of IELTS (Academic Test) 6.5 overall (with no less than 6.0 in any element) or equivalent.
TOEFL (iBT) 92 overall (minimum 20 in all elements)
CET- 4 (China) minimum score of 550
CET- 6 (China) minimum score of 520
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.