Section outline

  • Lecture: What is Machine Learning? Introductory concepts

    Data types: continuous, binary, categorical, survival, count: introduce datasets for each to be used in the course. Supervised vs. Unsupervised learning. Overview of prediction techniques for supervised learning. Regression tree and support vector machine overview example in R for binary data – discuss overfitting, tuning parameters. Discuss explanatory models vs. black box prediction (comparing regression tree with SVM). Provide an initial look at how to choose a predictive model.

    Faculty:  John Kornak

    Location: MH-1407