Introduction to Statistical Learning

Catalog description

A thorough overview of statistical learning, combining practical labs and real-world datasets to deepen understanding. Topics include: linear regression, classification, resampling methods, linear model selection and regularization, exploring non-linear models, tree-based methods, support vector machines, deep learning, survival analysis with censored data, unsupervised learning, and multiple testing techniques. Through hands-on experience and theoretical learning, students master the fundamentals of data analysis and gain insightful knowledge of the evolving field in statistical learning. Prerequisite knowledge: Basic knowledge of programming with Python or R. Repeatability: May not be repeated for credit. Grade Mode: Normal (A, B, C, D, F) Schedule Type (Primary): Lecture Click here for the Schedule of Classes.