Reinforcement Learning : An Introduction
Richard S. (University of Alberta) Sutton
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Gaussian Processes for Machine Learning
Carl Edward (University of Cambridge) Rasmussen
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Probabilistic Graphical Models : Principles and Techniques
Daphne (Stanford University) Koller
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Elements of Causal Inference : Foundations and Learning Algorithms
Jonas (Associate Professor of Statistics, University of Copenh Peters
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Introduction to Machine Learning
Ethem (Ozyegin University) Alpaydin
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Foundations of Machine Learning
Mehryar (New York University) Mohri
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Introduction to Natural Language Processing
Jacob (Assistant Professor, Google) Eisenstein
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Machine Learning from Weak Supervision : An Empirical Risk Minimization Approach
Masashi Sugiyama
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Introduction to Online Convex Optimization, second edition
Elad Hazan
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Machine Learning for Data Streams : with Practical Examples in MOA
Albert Bifet
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Bioinformatics : The Machine Learning Approach
Pierre (IDLAB, Inc.) Baldi
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Machine Learning in Non-Stationary Environments : Introduction to Covariate Shift Adaptation
Masashi (Associate Professor, Tokyo Institute of Technology) Sugiyama
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Machine Learning for Data Streams : with Practical Examples in MOA
Albert (Professor of Computer Science, Telecom ParisTech) Bifet
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Introduction to Statistical Relational Learning
Lise (Assistant Professor, University of Maryland College Park Getoor
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Learning Kernel Classifiers : Theory and Algorithms
Ralf Herbrich
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Foundations of Machine Learning
Mehryar (New York University) Mohri
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Introduction to Machine Learning
Ethem (OEzyegin University) Alpaydin
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Reinforcement Learning : An Introduction
Richard S. (University of Alberta) Sutton
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Bioinformatics : The Machine Learning Approach
Pierre Baldi
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Reinforcement Learning : An Introduction
Richard S. Sutton
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Graphical Models for Machine Learning and Digital Communication
Brendan J. Frey
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Probabilistic Graphical Models : Principles and Techniques
Daphne Koller
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Causation, Prediction, and Search
Peter Spirtes
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