Expressions of Interest for 2022 Journal Club

Introduction to Statistical Learning, with applications in R

(Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani)

Come join us for our “Introduction to Statistical Learning, with applications in R” book club in 2022. If you’re trying to make sense of complex data and want to feel more confident with statistical modelling options, then you’ll definitely want to join in! All levels welcome.

WEB Edition

Further information about the book:-

The book Introduction to Statistical Learning (ISL) arose from the need for a broad and less technical treatment of statistical models for learning from complex data. Beginning with Chapter 2, each chapter in ISL contains a lab illustrating how to implement the statistical learning methods seen in that chapter using the statistical software package R. These labs provide the reader with valuable hands-on experience.

ISL covers models for classification and regression, decision trees, boosting, support vector machines, and clustering. In this second edition of ISL, they include new chapters on deep learning (Chapter 10), survival analysis (Chapter 11), and multiple testing (Chapter 13).

The authors have based their book on the following four premises:

1. Many statistical learning methods are relevant and useful in a wide range of academic and non-academic disciplines, including the biological and biomedical sciences, and should be accessible to domain experts in disciplines outside of statistics.
2. Statistical learning should not be viewed as a series of black boxes. In this book, we carefully describe the model and the intuition behind it, model assumptions, and trade-offs between models/methods.
3. We have minimised discussion of technical details related to fitting procedures and theoretical properties. We assume that the reader is comfortable with basic mathematical concepts, but we do not assume a graduate degree in the mathematical sciences.
4. We presume that the reader is interested in applying statistical learning methods to real-world problems. In each lab, we walk the reader through a realistic application of the methods considered in that chapter using R.