STATS 315B: Modern Applied Statistics: Data Mining
PhD course, Stanford University, Department of Statistics
Spring 2023
Course description
This course provides a broad and deep treatment of modern statistical machine learning topics. Students entering the course are assumed to have foundational working knowledge in statistics, probability, and basic machine learning concepts, though the course has been designed to provide a broadly accessible treatment of the topics covered.
The course starts with a quick review of linear regression and classification, error metrics, and the bias-variance tradeoff. We then delve into decision trees and deep learning techniques for non-linear regression and classification tasks. The subsequent modules move beyond regression and classification tasks and turn to discovering patterns and low-dimensional structure via unsupervised learning. Topics include clustering, dimensionality reduction and autoencoding methods, and matrix factorization. The last module considers time series and sequential data sources via state space models and deep learning methods.