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. Computing will be done in R.
- Trevor Hastie Professor of Statistics
- 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
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.
Introductory courses in statistics or probability (e.g. STATS60), linear algebra (e.g. MATH51), and computer programming (e.g. CS105).