Machine Learning

Course Description

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, model/feature selection, learning theory, VC dimension, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search.

Course Details

  • Intensive Study: Computer Science
  • Enrollment requirements: This is a 4 unit course. Matriculated Stanford graduate students may enroll for 3 or 4 units but must still do the standard 4 units of coursework. Visiting students must enroll in 4 units. 
  • Limited Enrollment: Priority given to Stanford matriculated students.
  • Online Format: Asynchronous - This course is offered through a series of recordings and/or assignments that students can do at their own pace. Students will receive more information on the structure and expectations of the course as we get closer to the beginning of the Summer Quarter.

Prerequisites

Linear Algebra, and Basic Probability and Statistics.

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