Events

Concert: Ian Pace (Piano): Iberia

Tuesday 13th October 2026, 6:30pm

Performance Space, College Building, City St George’s, University of London

Admission price:

£5 - general admission (add to basket and purchase below)

Free - City Students and Staff, and Unwaged (sign up to attend here)

Concert: Marco Fusi (viola/viola d'amore) …stretched across its axes

Tuesday 24th November 2026, 7pm

Performance Space, College Building, City St George’s, University of London

Admission price:

£5 - general admission (add to basket and purchase below)

Free - City Students and Staff, and Unwaged (sign up to attend here)

Concert: Voices Under Pressure: Oskar McCarthy and Ben Smith

Tuesday 3th November 2026, 7pm

Performance Space, College Building, City St George’s, University of London

Admission price:

£5 - general admission (add to basket and purchase below)

Free - City Students and Staff, and Unwaged (sign up to attend here)

Conflict, Competition, Cooperation and Complexity: Using Evolutionary Game Theory To Model Realistic Populations

June 28-July 1, 2016, Vila Lanna, Prague, Czech Republic

Tickets:
Delegate:£262.50
Student Delegates:£112.50

http://www.city.ac.uk/department-mathematics/mathematical-biology/FourC-Modelling

Ticket price: £15

Date and time:
15th April 2015, 10:00-16:00

Location:
C309/310, Tait Building
City University London, Northampton Square, EC1V 0HB

For maps and directions please visit: http://www.city.ac.uk/visit

Learning Outcomes

By the end of this workshop, participants will be able to:

1. Describe the fundamental concepts of machine learning and distinguish them from traditional statistical approaches used in health research.
2. Prepare and explore real-world clinical or public health datasets for machine learning analysis, including handling missing data and selecting relevant predictors.
3. Apply common supervised machine learning techniques (e.g., logistic regression, decision trees, and random forests) to healthcare data using R/Python.
4. Evaluate and compare machine learning models using appropriate performance metrics such as accuracy, sensitivity, specificity, confusion matrices, and ROC curves.
5. Interpret machine learning outputs in clinically and epidemiologically meaningful ways to support evidence-based decision-making.
6. Recognise key ethical, methodological, and practical considerations in applying machine learning within healthcare and public health settings, including bias, fairness, and explainability.

Ticket prices

• City St George’s staff and students - £10.00
• External participants - £50.00