"This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"--
"This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding.
Inhaltsangabe
1 Introduction 1 I Foundations 29 2 Probability: Univariate Models 31 3 Probability: Multivariate Models 75 4 statistics 103 5 Decision Theory 163 6 Information Theory 199 7 Linear Algebra 221 8 Optimization 269 II Linear Models 315 9 Linear Discriminant Analysis 317 10 Logistic Regression 333 11 Linear Regression 365 12 Generalized Linear Models * 409 III Deep Neural Networks 417 13 Neural Networks for Structured Data 419 14 Neural Networks for Images 461 15 Neural Networks for Sequences 497 IV Nonparametric Models 539 16 Exemplar-based Methods 541 17 Kernel Methods * 561 18 Trees, Forests, Bagging, and Boosting 597 V Beyond Supervised Learning 619 19 Learning with Fewer Labeled Examples 621 20 Dimensionality Reduction 651 21 Clustering 709 22 Recommender Systems 735 23 Graph Embeddings * 747 A Notation 767