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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.
Date: Monday 5 – Friday 9 February 2024
Duration: 3 hours of live teaching per day
Time: 13:30 – 16:45 CET
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 teaching team 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.
By the end of this course, you will:
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.
4 credits - Engage fully in class activities and complete a post-class 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 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.
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).
Students should expect to spend 1-3 hours outside of the core teaching hours consolidating the material covered in these classes.