Richard S. Sutton

Andrew G. Barto

Francis Bach

Co-Director Autonomous Learning Laboratory Andrew G Barto

An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

An Introduction

Reinforcement Learning

Pierre Baldi

Professor Pierre Baldi

Søren Brunak

Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.

The Machine Learning Approach

Bioinformatics

Bernhard Schölkopf

Alexander J. Smola

Director of the Max Planck Institute for Intelligent in Tübingen Germany Professor for Machine Lea Bernhard Schölkopf

rnhard Schölkopf

Managing Director of the Max Planck Institute for Biological Cybernetics in Tubingen Germany Profe Bernhard Scholkopf

This volume provides an introduction to SVMs and related kernel methods. It provides concepts necessary to enable a reader to enter the world of machine learning using theoretical kernel algorithms and to understand and apply the algorithms that have been developed over the last few years.

Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels

Francis Bach

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