01
Machine Learning
Supervised and unsupervised learning and how models generalise.
ICDSS LECTURE SERIES
Machine Learning
Statistical learning, stochastic simulation and data science
- 7 lectures
- 14 hours of teaching
- Graded project
- Certificate on passing
- Fridays 18:00 to 20:00
Estimated workload: around 130 hours (comparable to a 5.25 ECTS module)
01 / About the series
Understand the methods.
Not just the library.
A rigorous lecture series covering topics from machine learning, data science, statistical learning and stochastic simulation.
The course is technical and mathematical, designed for students who want to understand how these methods really work, not just how to call them from a library.
02 / The syllabus
What you'll cover
02
Statistical Learning
The statistical theory behind learning from data.
03
Stochastic Simulation
Monte Carlo and simulation methods for modelling uncertainty.
04
Data Science
Working with real data end to end.
03 / Your lecturer
Ryan Leung
Lead Lecturer
Taught by Ryan Leung, a final-year Imperial mathematician who works as a quantitative researcher on Squarepoint Capital's volatility trading desk, using these exact methods day to day.
04 / Assessment & certificate
Put your learning
into practice
To complete the series you must submit a final project. Projects are graded, and you will receive a graded certificate from ICDSS upon passing.
05 / Who it's for
Open to all ICDSS members
Comfort with mathematics — calculus, linear algebra and probability — is recommended.
Free membership required
A free ICDSS membership is required to attend our events. People without a membership won't be allowed in.
06 / Schedule
Fridays, 18:00 to 20:00
Weekly from 30 October to 11 December 2026 · Room TBC
- Lecture 1Fri 30 Oct 202618:00 to 20:00
- Lecture 2Fri 6 Nov 202618:00 to 20:00
- Lecture 3Fri 13 Nov 202618:00 to 20:00
- Lecture 4Fri 20 Nov 202618:00 to 20:00
- Lecture 5Fri 27 Nov 202618:00 to 20:00
- Lecture 6Fri 4 Dec 202618:00 to 20:00
- Lecture 7Fri 11 Dec 202618:00 to 20:00