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Reinforcement learning : an introduction

Machine learning : a probabilistic perspective

Gaussian processes for machine learning

Introduction to machine learning

Foundations of machine learning

Introduction to natural language processing

Machine learning from weak supervision : an empirical risk minimization approach

Machine Learning in Non-Stationary Environments : Introduction to Covariate Shift Adaptation

Machine learning for data streams : with practical examples in MOA

Introduction to statistical relational learning

Learning Kernel Classifiers : Theory and Algorithms

Introduction to Statistical Relational Learning

Machine learning for data streams : with practical examples in MOA

Machine learning: a probabilistic perspective

Introduction to machine learning

Machine learning for data streams: with practical examples in MOA

Knowledge Graphs

Causation, Prediction, and Search

The Minimum Description Length Principle

Machine learning for data streams: with practical examples in MOA

Learning kernel classifiers : theory and algorithms

Introduction to online convex optimization

The minimum description length principle

Introduction to machine learning

Graphical Models for Machine Learning and Digital Communication

Reinforcement learning : an introduction

Learning in Graphical Models

Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond

Introduction to machine learning

Introduction to machine learning

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