Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such…mehr
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
Julian McAuley has been a Professor at UC San Diego since 2014. Personalized Machine Learning is the main research area of his lab, with applications ranging from personalized recommendation, to dialog, healthcare, and fashion design. He regularly collaborates with industry on these topics, including with Amazon, Facebook, Microsoft, Salesforce, and Etsy. His work has been selected for several awards, including an NSF CAREER award, and faculty awards from Amazon, Salesforce, Facebook, and Qualcomm, among others.
Inhaltsangabe
1. Introduction Part I. Machine Learning Primer: 2. Regression and feature engineering 3. Classification and the learning pipeline Part II. Fundamentals of Personalized Machine Learning: 4. Introduction to recommender systems 5. Model-based approaches to recommendation 6. Content and structure in recommender systems 7. Temporal and sequential models Part III. Emerging Directions in Personalized Machine Learning: 8. Personalized models of text 9. Personalized models of visual data 10. The consequences of personalized machine learning References Index.
1. Introduction Part I. Machine Learning Primer: 2. Regression and feature engineering 3. Classification and the learning pipeline Part II. Fundamentals of Personalized Machine Learning: 4. Introduction to recommender systems 5. Model-based approaches to recommendation 6. Content and structure in recommender systems 7. Temporal and sequential models Part III. Emerging Directions in Personalized Machine Learning: 8. Personalized models of text 9. Personalized models of visual data 10. The consequences of personalized machine learning References Index.
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