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Member rate £492.50
Non-Member rate £985.00
* If you attended our Methods School during the calendar years 2024 or 2025, you qualify for £45 off your course fee.
Date: Monday 19 – Friday 23 May 2025
Time: 14:00 – 17:00 CEST
This course offers a highly interactive online learning environment, using state of the art online pedagogical tools. It is designed for undergraduate (if they have prior statistics/regression training), Master's, PhD and postdocs. The capacity of the course is limited to 16 so that the instructor can cater to the needs of each participant.
Throughout the course, you will learn the fundamentals of machine learning methods including regularisation, cross-validation, as well as major model forms, as they apply to our scientific study of political systems. You will focus largely on supervised methods of machine learning.
By the end of this course, you will:
The course will equip you with advanced knowledge and skills to develop convincing and important research designs, in political science, using advanced computational methods.
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
Thomas Robinson is a methodologist and political scientist, whose research focuses on the application of machine learning (ML) methods within experimental research.
His current projects include using deep learning to create synthetic data, using ML to understand how individuals around the world assess government performance, and assessing voters' ability to identify corrupt candidates in elections.
Tom is an Assistant Professor in the Methodology Department at LSE, and obtained his doctorate in Politics from the University of Oxford.
Day 1: What is machine learning?
Day 2: Regularised methods and the bias variance trade-off
Day 3: Tree-based methods and hyperparameter tuning
Day 4: Neural networks and feature engineering
Day 5: Ensemble learning
The course is structured into five live Zoom sessions, each lasting three hours. The first 1.5-2 hours will focus on the major theoretical components of each day’s topic. The remaining hour will be spent walking through hands-on coding exercises, where you will apply the concepts and methods we discuss in the lecture to real-world data.
Prior to each session, the instructor will distribute the slides and R script for you to explore at your own pace. During the session, the instructor will go through the code and models with you. We will also take time to discuss the benefits and limitations of machine learning models, as a group.
After each session, there will be an exercise for you to complete, which will help consolidate your understanding of both the theoretical and practical topics we cover.
The instructor will also conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
You must have a basic understanding of statistical analysis, including (linear) regression. We will build extensively on these statistical concepts in the early components of this course. While we will occasionally consider mathematical formulae, knowledge of linear algebra is not required.
This course will also include guided coding exercises using R. To gain the most from this course, you should have some basic proficiency in R. For example, you should be able to manipulate vectors, write for-loops, and use conditionals (e.g. if-then statements).
You will engage in a variety of activities designed to deepen your understanding of the subject matter. While the cornerstone of your training experience will be daily live teaching sessions, the learning commitment will extend beyond these. This ensures that you engage deeply with the course material, partcipate actively, and complete assessments to solidify your learning.
If you have registered and paid for the course, you will be given access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you can view course materials such as pre-course readings. You will be expected to commit approximately 20 hours per week leading up the start date to familiarise yourself with the content and complete any pre-course tasks.
During the course week, you will need to dedicate approximately 1–3 hours per day to prepare and work on assignments.
Each course offers the opportunity to earn three ECTS credits. Should you wish to earn a fourth credit, you will need to complete a post-course assignment, which will involve approximately 25 hours of work.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.