Dr Thomas Robinson

This course is available on the MPA in Data Science for Public Policy, MSc in Applied Social Data Science, MSc in Econometrics and Mathematical Economics, MSc in Geographic Data Science and MSc in Social Research Methods. This course is available as an outside option to students on other programmes where regulations permit.

This course is not controlled access. If you register for a place and meet the prerequisites, if any, you are likely to be given a place.

Applied Regression Analysis (MY452) or equivalent is required. Students should understand basic linear algebra and know at least one programming language. If this programming language is not R, students should take the Digital Skills Lab course in R before the start of term.

Machine learning uses algorithms to find patterns in large datasets and make predictions based on them. This course will use prominent examples from social science research to cover major machine learning tasks including regression, classification, clustering, and dimensionality reduction. Lectures will use case studies to introduce common machine learning strategies including regularised regression (e.g. LASSO), tree-based methods, clustering algorithms neural networks. As part of this course, students will consider ethical issues surrounding machine learning applications, including privacy and algorithm bias. Students will learn to apply algorithms to data and to validate and evaluate models. Students will work directly with social data and analyse these data using Python or R.

This course is delivered through a combination of classes and lectures totalling a minimum of 20 hours across Winter Term. 

This course has a reading week in Week 6 of WT.

Students will be expected to produce 1 problem sets in WT and will complete 5 quizzes in WT.

The problem set will build on the first week of the course. Students will start the problem set in the computer workshop sessions and complete it outside of class.

• Géron, A. (2017). Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc.
• Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc.
• Conway, D., & White, J. (2012). Machine Learning for Hackers. O'Reilly Media, Inc.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (Vol. 112). New York: Springer.
• Cantú, F., & Saiegh, S. M. (2011). Fraudulent democracy? An analysis of Argentina's Infamous Decade using supervised machine learning. Political Analysis, 19(4), 409-433.
• Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. Proceedings of the Eleventh International AAAI Conference on Web and Social Media (ICWSM 2017), 512-515.
• D'Orazio, V., Landis, S. T., Palmer, G., & Schrodt, P. (2014). Separating the wheat from the chaff: Applications of automated document classification using support vector machines. Political Analysis, 22(2), 224-242.
• Jones, Z. M., & Lupu, Y. (2018). Is There More Violence in the Middle?. American Journal of Political Science, 62(3), 652-667.
• Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 201218772.
• Wang, Y., & Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology, 114(2), 246-257.

Problem sets (100%) in the WT.

Two summative problem sets (100%) in WT.