Dr Blake Miller COL.7.14

This course is available on the MPhil/PhD in Social Research Methods. This course is available with permission as an outside option to students on other programmes where regulations permit.

This course is available to research students only.

Applied Regression Analysis (MY452) or equivalent is required.

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. A particular emphasis will be placed on the ethical issues surrounding machine learning applications, including privacy, algorithmic bias, and informed consent. Lectures will use case studies to introduce specific machine learning algorithms including LASSO, ridge regression, logistic regression, k-nearest neighbour classification, decision trees, support vector machines, k-means clustering, hierarchical clustering, principal component analysis, and linear discriminant analysis. Students will learn to apply these algorithms to data and validate and evaluate models. Students will work directly with social data and analyse these data using Python or R.

20 hours of lectures and 10 hours of computer workshops in the MT.

The workshops will last two hours and take place every two weeks.

Students will be expected to produce 1 problem set in the LT.

One structured problem set will be provided in the first weeks of the course. Students will start the problem set in the first computer workshop session 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.

In-class assessment (40%) in the MT.
Take-home assessment (60%) in the LT.

Four structured problem sets will be marked and will provide 40% of the mark. For the take home exam, students will be expected to submit a 3000-word paper in which they identify and contextualize a relevant social data science problem related to their dissertation research, find suitable data to address it, plan and conduct extensive machine learning analysis on the data, and present the findings.

Marking of these assessments will be at a level appropriate for PhD students.

Department: Methodology

Total students 2018/19: Unavailable

Average class size 2018/19: Unavailable

Value: Half Unit