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Dr Joshua Loftus

This course is compulsory on the BSc in Data Science. This course is available on the BSc in Actuarial Science, BSc in Finance, BSc in Mathematics with Data Science, BSc in Mathematics with Economics, BSc in Mathematics, Statistics and Business and BSc in Politics and Data Science. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.

This course cannot be taken with ST309 Elementary Data Analytics.

Students must have completed either ST102, or ST109 and EC1C1, as well as a second-year course covering regression analysis.

Previous programming experience is not required but students who have no previous experience in R must complete an online pre-sessional R course from the Digital Skills Lab before the start of the course (https://moodle.lse.ac.uk/course/view.php?id=7745)

The primary focus of this course is on the core machine learning techniques in the context of high-dimensional or large datasets (i.e. big data). The first part of the course covers elementary and important statistical methods including nearest neighbours, linear regression, logistic regression, regularisation, cross-validation, and variable selection. The second part of the course deals with more advanced machine learning methods including regression and classification trees, random forests, bagging, boosting, and deep neural networks. The course will also introduce causal inference motivated by analogy between double machine learning and two-stage least squares. All the topics will be delivered using a combination of illustrative real data examples and simulations. Students will also gain hands-on experience using R or Python (programming languages and software environments for data analysis, computing and visualisation).

13 hours and 20 minutes of lectures and 16 hours and 40 minutes of seminars in the AT.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours in Autumn Term.

[This course includes a reading week in Week 6 of AutumnTerm].

Students are required to install R/RStudio in their own laptops. 

Student not having a laptop of their own will be offered to use personal computers available in seminar rooms.

Students will be expected to produce 3 problem sets in the AT.

The first two problem sets will be formative and allow students to practice for the third, summative problem set.

• James, G., Witten, D., Hastie, T. and Tibshirani, R. An Introduction to Statistical Learning with Applications in R. Springer, 2021.
• Hardt, M. and Recht, B. Patterns, Predictions, and Actions: Foundations of Machine Learning. Princeton University Press, 2022.
• Chernozhukov, V., Hansen, C., Kallus, N., Spindler, M., and Syrgkanis, V. Applied Causal Inference Powered by ML and AI. Online, 2024.
• Wickham, H., Çetinkaya-Rundel M., and Grolemund, G. R for Data Science. O'Reilly, 2023.

Coursework (20%) in the AT.
Project (50%) in the period between AT and WT.
Group project (30%) in the ST.

Students are required to submit a group project by applying machine learning methods covered in this course on some real data using R (which accounts for 30% of the final assessment), and an individual project that includes a prediction competition component (which accounts for 50% of the final assessment).

In addition to some real data examples, the focus of this course is to introduce some theoretical and methodological concepts in machine learning. These components will be tested by coursework as problem sets (which account for 20% of the final assessment).