Session 6 Machine learning: Introduction

• Learning outcomes: Learn…
  • …for what kind of tasks ML is (successfully) used.
  • …about the fundamental concepts underlying ML
    • Prediction, classification, supervised, unsupervised learning, model accuracy etc.
  • …how to predict in R (using a simple logistic regression).
  • …how to use training and test data for ML in R.
  • …how to assess accuracy in R (logistic regression).

The material for the following sessions is based on James et al. (2013), Molina and Garip (2019), James et al. (2013), Chollet and Allaire (2018) and others’ work (including some of my own).

References

Chollet, Francois, and J J Allaire. 2018. Deep Learning with R. 1st ed. Manning Publications.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics. Springer.

Molina, Mario, and Filiz Garip. 2019. “Machine Learning for Sociology.” Annu. Rev. Sociol., July.