Statistical and Machine Learning

Course #OS4118

Est.imated Completion Time: 3 months


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.

Included in degrees & certificates

  • 268
  • 367


  • OA4106
Offerings database access
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Academic Calendar

  •  09 Jun 2023

    Spring quarter last day of classes

  •  13 Jun 2023

    Spring quarter final examinations begin

  •  15 Jun 2023

    Spring quarter final examinations end