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Machine Learning Methods for Political Science

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in July/August 2023 or February 2024.

Course Dates and Times

Date: Monday 29 July – Friday 2 August 2024
Time: 15:00 – 18:00 CEST

Thomas Robinson

The London School of Economics & Political Science

This course will provide you with a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the instructor can cater to the specific needs of everyone.

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. Emphasis will be placed on critically engaging with when machine learning can enhance our research at both design and analysis stages. 

Purpose of the Course

By the end of this course, you will:

  • understand and implement fundamental concepts in the use of machine learning;
  • distinguish between major machine learning model types (including regression-, tree- and network-based forms);
  • construct and run machine learning methods on social scientific datasets;
  • compare and critique the appropriateness of machine learning methods for various use-cases in political science (and the wider social sciences).

Overall, the course will equip you with advanced knowledge and skills that will help you develop convincing and important research designs, in political science, using advanced computational methods.

ECTS Credits

3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.

Instructor Bio

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.


Key topics covered

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 

How the course will work

The course is structured into five live Zoom sessions, each lasting 3 hours. The first 1.5-2 hours will focus on the major theoretical components of each day’s topic. The remaining 1 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. Additionally, we will 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.

Prerequisite Knowledge

Students intending to take this course 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).

Learning commitment

As a participant in this course, you will engage in a variety of learning activities designed to deepen your understanding and mastery of the subject matter. While the cornerstone of your learning experience will be the daily live teaching sessions, which total three hours each day across the five days of the course, your learning commitment extends beyond these sessions.

Upon payment and registration for the course, you will gain access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you will have access to course materials such as pre-course readings. The time commitment required to familiarise yourself with the content and complete any pre-course tasks is estimated to be approximately 20 hours per week leading up to the start date.

During the course week, you are expected to dedicate approximately one-three hours per day to prepare and work on assignments.

Each course offers the opportunity to be awarded three ECTS credits. Should you wish to earn a 4th credit, you will need to complete a post-course assignment, which will involve approximately 25 hours of work.

This comprehensive approach ensures that you not only attend the live sessions but also engage deeply with the course material, participate actively, and complete assessments to solidify your learning.


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.