Session Overview

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.

In this talk, we'll introduce a common set of principles and methods that underpin the design of personalized predictive models. We'll begin by revising "traditional" forms of personalized learning, such as recommender systems. Later, we'll see how similar ideas apply to domains such as natural language processing and computer vision. Finally, we'll study the consequences and risks of deploying personalized predictive systems.


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    Personalized Machine Learning

    • Abstract & Bio

    • Personalized Machine Learning


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