Adaptive Computation and Machine Learning series – serie

Reinforcement Learning

An Introduction

Introduction to Machine Learning

Elements of Causal Inference

Foundations and Learning Algorithms

Gaussian Processes for Machine Learning

Probabilistic Graphical Models

Principles and Techniques

Machine Learning

A Probabilistic Perspective

Deep Learning

Foundations of Machine Learning

Introduction to Natural Language Processing

Introduction to Online Convex Optimization, second edition

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

Machine Learning in Non-Stationary Environments

Introduction to Covariate Shift Adaptation

Bioinformatics

The Machine Learning Approach

Machine Learning for Data Streams

with Practical Examples in MOA

Principles of Data Mining

Semi-Supervised Learning