Applied Machine Learning


Fall 2018

Wednesdays, 6:00 - 7:30 pm; Keohane 4B, 402SEM

Introduction to topics in machine learning through an applied perspective. The course assumes basic fluency in programming and mathematics at the single-variable calculus level, and will include learning specific machine learning concepts (listed below), their historical origins, and existing and potential applications to modern society.

Machine learning concepts studied will include: classification (including naive Bayes, support vector machines, kernel methods, and neural networks), regression (including spline interpolation and linear and polynomial regression), mixture of Gaussians clustering, object detection (including convolutional neural networks, feature extraction, edge detection, and processing methods), generalized discriminant analysis, and evaluation of machine learning models.

  • Shrey Gupta,
  • Rebecca Steorts, Statistical Science
Class Limit: