Richard Berk Knihy

This textbook explores statistical learning applications focused on the conditional distribution of a response variable based on predictors, particularly when a credible pre-analysis model is lacking. It highlights that effective statistical learning relies on integrated data collection, management, statistical procedures, and result interpretation. The core theme positions supervised learning as a form of regression analysis, illustrated through numerous real applications and R code, emphasizing practical implications. The text reflects the growing intersection of computer science and statistics, acknowledging the tensions that may arise. The third edition incorporates significant recent advancements, including conceptual frameworks for statistical learning, the influence of "big data," and the implications of post-model selection inference. It addresses challenges to statistical inference posed by statistical learning and emphasizes the relationship between data collection and analysis. Ethical and political issues related to algorithmic methods are discussed, focusing on transparency, fairness, and accuracy. New sections on these topics and a chapter on deep learning enhance the content, alongside expanded discussions on precursors to deep learning, fitting, forecasting, and estimation targets for algorithmic methods. Resampling procedures are also emphasized. The material is tailored for upper undergraduate and graduate st