"Machine learning encompasses a broad range of problems ranging from detecting objects in images, finding documents relevant to a given query, or predicting the next element in a sequence, among countless others. Traditional approaches to these problems operate by collecting large, labeled datasets for training, uncovering informative features, and mining complex patterns that explain the association between features and labels. Typically, labels are regarded as an underlying 'truth' that should be predicted as accurately as possible"--
"Machine learning encompasses a broad range of problems ranging from detecting objects in images, finding documents relevant to a given query, or predicting the next element in a sequence, among countless others. Traditional approaches to these problems operate by collecting large, labeled datasets for training, uncovering informative features, and mining complex patterns that explain the association between features and labels. Typically, labels are regarded as an underlying 'truth' that should be predicted as accurately as possible"--
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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