Personalized Machine Learning: Online Supplement

The book is currently available in draft form as a downloadable pdf. Every day we interact with machine learning systems that personalize their predictions to individual users, whether to recommend movies, find new friends or dating partners, or organize our news feeds. Such systems involve several modalities of data, ranging from sequences of clicks or purchases, to rich modalities involving text, images, or social interactions. While settings and data modalities vary significantly, in this book we introduce a common set of principles and methods that underpin the design of personalized predictive models. The book begins by revising "traditional" machine learning models, with a special focus on how they should be adapted to settings involving user data.