Introduction to Statistical Learning


Stanford School of Humanities and Sciences



New techniques have emerged for both predictive and descriptive learning that help us make sense of vast and complex data sets. The particular focus of this course will be on regression and classification methods as tools for facilitating machine learning. This course is in a flipped format: there will be pre-recorded lectures and in-class problem solving and discussion sessions will be used.

NOTE: Students will be required to use R and R Studio (preferred) in this course.



Introductory courses in statistics or probability (e.g. STATS60), linear algebra (e.g. MATH51), and computer programming (e.g. CS105).

Topics include

  • Overview of statistical learning
  • Linear regression
  • Classification
  • Resampling methods
  • Linear model selection and regularization
  • Moving beyond linearity
  • Tree-based methods
  • Support vector machines
  • Unsupervised learning

Note on Course Availability

This course is typically offered Winter quarter.

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.

008 Winter 2019-20 Online

Enroll Now

Dates:January 6 - March 13, 2020
Days: Mon
Units: 3.00
Instructors: Oleg Melnikov
Delivery Option:
For Credit $3,900.00 ?


Enrollment Dates: October 27 to December 9, 2019

This course may not currently be available to learners in some states and territories.