Introduction to Statistical Learning
Fee may apply
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).
- Overview of statistical learning
- Linear regression
- 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.
|Dates:||January 6 - March 13, 2020|
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
NotesEnrollment Dates: October 27 to December 9, 2019
This course may not currently be available to learners in some states and territories.