OS4118 Statistical and Machine Learning

This course introduces students to the art and science of statistical and machine learning to find patterns in large and "Big" data. The focus is on the strengths and weaknesses of learning techniques and their implementation. Fundamental ideas common to learning methods are covered, and supervised/unsupervised techniques are introduced. These techniques include: re-sampling methods, advanced clustering and visualization, tree-based ensembles, stochastic gradient boosting, deep neural networks, auto-encoding and other dimension reduction techniques, and applications to natural language processing. The software package R and high-performance parallel or distributed computing will be used to demonstrate these techniques.

Prerequisite

OA4106 or consent of instructor

Lecture Hours

3

Lab Hours

0

Quarter Offered

  • As Required
  • Summer