Adaptive Computation and Machine Learning series – serie

Reinforcement Learning

An Introduction

Deep Learning

Probabilistic Graphical Models

Principles and Techniques

Machine Learning in Non-Stationary Environments

Introduction to Covariate Shift Adaptation

Machine Learning

A Probabilistic Perspective

Bioinformatics

The Machine Learning Approach

Elements of Causal Inference

Foundations and Learning Algorithms

Machine Learning for Data Streams

with Practical Examples in MOA

Foundations of Machine Learning

Introduction to Natural Language Processing

Introduction to Machine Learning

Introduction to Online Convex Optimization, second edition

Principles of Data Mining

Gaussian Processes for Machine Learning

Semi-Supervised Learning

Boosting

Foundations and Algorithms

Minimum Description Length Principle

Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond

Introduction to Statistical Relational Learning

Learning Kernel Classifiers

Theory and Algorithms

Machine Learning for Data Streams

with Practical Examples in MOA