Introduction to Statistical Learning
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 (STATS60 or equivalent), linear algebra (MATH51 or equivalent), and computer programming (CS105 or equivalent).
A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.
- Overview of statistical learning
- Linear regression
- Resampling methods
- Linear model selection and regularization
- Moving beyond linearity
- Tree-based methods
- Support vector machines
- Unsupervised learning
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