Topics
The Statistical Learning Framework
The Role of Regularization: Ridge Regression
Pitfalls of High-Dimensional Data
Feature Selection and Sparse Regression
References
These references provide more details about the topics we cover:
Data Science as a science
Learning Theory
- Chapters 1,2 and 5 of Shalev-Shwartz,
Shai, and Shai Ben-David. “Understanding machine learning: From theory
to algorithms”, Cambridge University Press, 2014 (PDF
available).
- Sections 2.1-2.2 of James,
Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An
Introduction to Statistical Learning”, Vol. 112. Springer, 2013 (PDF
available).
Preprocessing of data
- Chapter 7 of James, Gareth,
Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An Introduction
to Statistical Learning”, Vol. 112. Springer, 2013 (PDF
available).
- Chapter 3 and 4 of Müller,
Andreas C., and Sarah Guido. “Introduction to machine learning with
Python: a guide for data scientists”, O’Reilly, 2016. This reference
focusses more on the practical aspects.
Ridge and Sparse Regression
- Chapter 6 of James, Gareth,
Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An Introduction
to Statistical Learning”, Vol. 112. Springer, 2013 (PDF
available).
- Chapter 9 of Shalev-Shwartz,
Shai, and Shai Ben-David. “Understanding machine learning: From theory
to algorithms”, Cambridge University Press, 2014 (PDF
available).
- Chapter 10 of Mohri,
Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. “Foundations of
Machine Learning”, MIT press, 2018.