Personalized Machine Learning
- Length: 350 pages
- Edition: 1
- Language: English
- Publisher: Cambridge University Press
- Publication Date: 2022-02-03
- ISBN-10: 1316518906
- ISBN-13: 9781316518908
- Sales Rank: #3381341 (See Top 100 Books)
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.
Frontmatter Contents Preface Introduction Machine_Learning_Primer Regression_and_Feature_Engineering Classification_and_the_Learning_Pipeline Fundamentals_of_Personalized_Machine_Learning ntroduction_to_Recommender_Systems Model-Based_Approaches_to_Recommendation Content_and_Structure_in_Recommender_Systems Temporal_and_Sequential_Models Emerging_Directions_in_Personalized_Machine_Learning Personalized_Models_of_Text Personalized_Models_of_Visual_Data The_Consequences_of_Personalized_Machine_Learning References Index
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