Data Mining and Statistical Learning

2015, Trinity College, Dublin, Department of Political Science

Instructor: Prof Kenneth Benoit, LSE

Details: Class meets MONDAYS in Feb-March from 14:00 – 16:30, with one exception on Day 2 (see below)

Rooms: See specific dates below.

Note: As the class proceeds, I will add resources (slides, R code, text datasets, problem sets) to each session below.

Main Texts:

  • James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Springer Science & Business Media.

  • Lantz, Brett. 2013. Machine Learning with R. Packt Publishing Ltd.

  • Zumel, N., & Mount, J. 2014. Practical data science with R. Shelter Island, NY: Manning.

Detailed Schedule

Day 1 Working with data and data structures

(Mon 9 Feb, 14:00-16:30, Room 201 Pheonix House)

  • Datasets, databases, data formats, transforming and organizing data. Review of R data structures, SQL and alternatives.
  • Required readings: Lantz Ch. 2; Zumel and Mount Ch. 2;
  • Recommended readings:
  • Exercise 1, due Wed Mar 25. Answer key here.

Day 2 Rethinking regression as a predictive tool (Wed 25 Mar, 10:00-12:30, Arts Block 3025)

  • Revisiting prediction for the classical regression model, including logistic regression.  Prediction v. association and causation.

  • Required Readings

    • James et al, Chs 3-4
    • Lantz, Ch. 6
    • Recommended readings:
    • Conway, Drew, and John White. 2012. Machine Learning for Hackers. O’Reilly. Chapter 5, “Regression: Predicting Page Views”.
    • Zumel and Mount, Ch. 7
  • Exercises: None, due to the short week, prediction methods will be rolled into the exercise for week 3.

Day 3 Introduction to machine learning (Mon 2 Mar, 14:00-16:30, 206 Pheonix House)

Day 4  Shrinkage methods (Mon 9 Mar, 14:00-16:30, 206 Pheonix House)

  • Ridge regression, the Lasso

  • Readings:

    • James et al, Ch 6
    • Recommended readings:
    • Conway, Drew, and John White. 2012. Machine Learning for Hackers. O’Reilly. Chapter 6, “Regularization: Text Regression”.
  • Exercise 3:

    1. Using the dail2002.dta dataset, select a random subset of 80% of the candidates, and then stepwise methods to discover the or a model that maximizes the variation explained in this training dataset. Then predict the fit to the 20% that you left out, and report the RMSE.
    2. Following the worked examples from James et al Ch. 6, do Problem 9 from p263 using the College dataset. You can get this from the “ISLR” package.

Day 5  Unsupervised learning (Mon 16 Mar, 14:00-16:30,Aras an Phiarsigh Room 2.04)

  • Principal components, clustering methods.

  •   review the last part of James et al, Ch 6 on principal components regression
  •   James et al Ch 10
  •   Bond, Robert, and Solomon Messing. 2015. “Quantifying Social Media’s Political Space: Estimating Ideology From Publicly Revealed Preferences on Facebook.” _American Political Science Review_ 109(01): 62–78.
  •   Weller, Susan C, and A Kimball Romney. 1990. _Metric Scaling: Correspondence Analysis_. Sage.

Day 6  Working with text (Mon 23 Mar, 14:00-16:30, Room 201 Pheonix House)


Ken Benoit
Ken Benoit
Professor of Computational Social Science
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