As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.
Datasheets for Datasets
The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose...
Data Integration
This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem.
The Roadmap of Mathematics for Machine Learning
A complete guide to linear algebra, calculus, and probability theory
Coding Club: a positive peer-learning community
Free and accessible tutorials on data analysis, manipulation, visualisation and more.
Datasets Guide | Unsloth Documentation
Learn how to create & prepare a dataset for fine-tuning.
Personalized Machine Learning
This page contains collects information and supplementary material for my textbook Personalized Machine Learning:
Active Learning with Statistical Models
The Data Minimization Principle in Machine Learning
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. Rooted in...
SoK: Data Minimization in Machine Learning
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulations like GDPR...
Introducing beginners to the mechanics of machine learning – Miriam Posner
Every year, I spend some time introducing students to the mechanics of machine learning with neural nets. I definitely don’t go into great depth; I usually only have one class for this. But I try to unpack at least some of the major concepts, so that ML isn’t quite such a black box.
Categories for Machine Learning
This seminar series seeks to promote the learning and use of Category Theory by Machine Learning Researchers
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
Data Shapley has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. It can effectively identify helpful or harmful data points for a learning algorithm. In this paper, we propose Beta Shapley, which is a substantial generalization of Data Shapley. Beta Shapley arises naturally by relaxing the efficiency axiom of the Shapley value, which is not critical for machine learning settings. Beta Shapley unifies several popular data valuation methods and includes data Shapley as a special case. Moreover, we prove that Beta Shapley has several desirable statistical properties and propose efficient algorithms to estimate it. We demonstrate that Beta Shapley outperforms state-of-the-art data valuation methods on several downstream ML tasks such as: 1) detecting mislabeled training data; 2) learning with subsamples; and 3) identifying points whose addition or removal have the largest positive or negative impact on the model.