Introduction to Probability for Computer Scientists

Course Description

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms.

Course Details

  • Grading Basis: Letter Grade or Credit/No Credit
  • Unit-Range Information: Matriculated Stanford graduate students may enroll for 3, 4, or 5 units; everyone else must take the course for 5 units. All students do 5 units worth of work, including Stanford graduate students enrolled for 3 or 4 units.
  • Intensive Studies: This course is offered as part of the Data Science and Computer Science Intensives and must be taken for 5 units. Although it is a qualifying course for two Intensives, it will only count towards one Intensive's course requirements. See the Intensive Studies page for more information on how to receive an official Document of Completion.

Prerequisites

CS 103, 106B, or X; multivariate Calculus at the level of MATH 51, CME 100, or equivalent

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