Course Description
This course introduces fundamental statistical machine learning concepts and tools using Python. Emphasis is placed on the following subjects: descriptive statistics, statistical distributions, random number generation, basic data visualization; linear regression; basic classification; error estimation: cross-validation, bias-variance trade-off; shrinkage methods; dimension reduction; beyond linearity: smoothing splines, local regression, additive models; tree and ensemble methods; powerful classifiers; unsupervised learning.
Prerequisites
Corequisites
Schedule
| This Course was not Offered During Spring/Summer 2025 Term |
| This Course was not Offered During Fall 2025 Term |
| The tentative timetable is not yet available for the Winter 2026 Term |
| The tentative timetable is not yet available for the Spring/Summer 2026 Term |
