Course Name: Statistical Learning for Public Policy I
Course Number: ECON0127
Lecture: Wednesday, 11:00-1:00 PM – Room
Seminar: Monday, 2:00-3:00 PM and 4:00-5:00 PM – Room
Credits: 15

Instructor: Moritz Marbach
Office: 36-38 Gordon Square, Room 303
Student Support and Feedback Hours: Office 365 Bookings

Teaching Assistant: Ruolei Zhu
Student Support and Feedback Hours: Mondays, 3-4 PM


This course introduces students to a wide range of statistical methods. Covering the first half of the textbook An Introduction to Statistical Learning, the main focus of this course is on linear models, generalized linear models, and multilevel models. The course has a strong practical focus, enabling students to apply the methods to real world data.

Lecture Seminar Topic Readings
05.10.2022 10.10.2022 Introduction ISL: 1,2
12.10.2022 17.10.2022 Linear Regression I ISL: 3.1-3.2
19.10.2022 24.10.2022 Linear Regression II ISL: 3.3-3.5
26.10.2022 31.10.2022 Classification I ISL: 4.1-4.3, 4.6
02.11.2022 14.11.2022 Classification II ISL: 4.4-4.5
16.11.2022 21.11.2022 Model Selection I ISL: 5, 6.1
23.11.2022 28.11.2022 Model Selection II ISL: 6.2-6.4
30.11.2022 05.12.2022 Multilevel Models I GH: 1,11
07.12.2022 12.12.2022 Multilevel Models II GH: 12
14.12.2022 Review

Textbooks and Readings

Main textbooks:

  • (ISL) G. James, D. Witten, T. Hastie and R. Tibshirani. 2021 (2nd ed.). An Introduction to Statistical Learning. Springer. The PDF of the book is freely available at:

  • (GH) A. Gelman and J. Hill. 2006. Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

Additional readings:

  • T. Hastie, R. Tibshirani, and J. Friedman. 2009 (2nd ed.). The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer.

Class Assignments

You are expected to complete the assigned chapters before each lecture, and the problem sets before the seminar. The problem sets do not count toward your course mark. I encourage you to form learning groups to help each other working through the problem sets.


Throughout the course we will use the free and open source statistical analysis software R. Before the course starts, you should download and install R from on your personal computer. Note that R can not run on tablets.

It is also useful to install a modern Integrated Development Environment (IDE) for R. An IDE is a program that helps you write and edit code efficiently. If you do not have any programming experience, we recommend downloading and installing RStudio which is an R-specific IDE:

Another popular IDE that works well with R (and many other programming languages) is VS Code.


Written exam (3 hours) covering all materials.


No formal requirements.

Academic Freedom

Academic freedom is the cornerstone of university research and teaching, so that all university staff, speakers, and students can freely explore questions and ideas and challenge perceived views and opinions, without being censored or harassed by a government, any state authorities, the University, other students, or external pressure groups. As part of the UCL academic community, all staff, speakers, and students share these responsibilities:

  • Everyone must respect freedom of thought and freedom of expression. Your lecturer will not limit what can be discussed in the seminar, as long as it is relevant to the subject. They will not censor any topics, and they will expose you to controversial issues, questions, facts, views, and debates.

    • You may disagree with some facts or views that you read or hear in the classroom. You are encouraged to engage with these facts and views in a respectful manner.

    • Your lecturer will not penalise you merely for expressing views they or other students disagree with. However, they will expect you to present logical arguments supported by evidence.

  • You are explicitly prohibited from recording, publishing, distributing or transferring any class material/content, in whole or in part, in any format, to any individual or entity outside the module, linking to or posting it online (including social media), or making it otherwise available to any person or entity outside the module, unless you have received prior specific written approval from the module leader. You are also explicitly prohibited from aiding or abetting in any of these actions. Similarly, your lecturer will not record, publish or distribute seminar sessions without the explicit consent of the participants.

By agreeing to take this module, you agree to abide by these terms. If you do not comply with these terms, you will potentially be subject to disciplinary actions similar to those under violations of the university Student Code of Conduct.


Photo copyright: Tony Slade, University College London 2015

Slides are based on the slide set shared by the authors of An Introduction to Statistical Learning and by Dominik Hangartner. I am also in-depth to Achim Ahrens and Dalston Ward for several improvements to the problem sets.