Marc Peter Deisenroth's Books

Mathematics for Machine Learning

By: Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong

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Mathematics for Machine Learning

By: Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

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Page Count

392

Publisher

Cambridge University Press

Published Date

ISBN 10

1108569323

ISBN 13

9781108569323

Efficient Reinforcement Learning Using Gaussian Processes

By: Marc Peter Deisenroth

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Efficient Reinforcement Learning Using Gaussian Processes

By: Marc Peter Deisenroth

This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.

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Page Count

226

Publisher

KIT Scientific Publishing

Published Date

ISBN 10

3866445695

ISBN 13

9783866445697

Mathematics for Machine Learning

By: Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong

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Mathematics for Machine Learning

By: Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

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Page Count

391

Publisher

Cambridge University Press

Published Date

ISBN 10

1108470041

ISBN 13

9781108470049

Distributional Reinforcement Learning

By: Marc G. Bellemare,Will Dabney,Mark Rowland

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Distributional Reinforcement Learning

By: Marc G. Bellemare,Will Dabney,Mark Rowland

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment. The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.

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Page Count

385

Publisher

MIT Press

Published Date

ISBN 10

0262048019

ISBN 13

9780262048019

Linear Estimation in Interconnected Sensor Systems with Information Constraints

By: Reinhardt, Marc

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Linear Estimation in Interconnected Sensor Systems with Information Constraints

By: Reinhardt, Marc

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed.

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Page Count

262

Publisher

KIT Scientific Publishing

Published Date

ISBN 10

3731503425

ISBN 13

9783731503422

A Survey on Policy Search for Robotics

By: Marc Peter Deisenroth,Gerhard Neumann,Jan Peters

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A Survey on Policy Search for Robotics

By: Marc Peter Deisenroth,Gerhard Neumann,Jan Peters

A Survey on Policy Search for Robotics provides an overview of successful policy search methods in the context of robot learning, where high-dimensional and continuous state-action space challenge any Reinforcement Learning (RL) algorithm. It distinguishes between model-free and model-based policy search methods.

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Page Count

160

Publisher

Foundations and Trends(r) in R

Published Date

ISBN 10

1601987021

ISBN 13

9781601987020

Book's by Marc Peter Deisenroth

Mathematics for Machine Learning

Mathematics for Machine Learning

Efficient Reinforcement Learning Using Gaussian Processes

Efficient Reinforcement Learning Using Gaussian Processes

Mathematics for Machine Learning

Mathematics for Machine Learning

Distributional Reinforcement Learning

Distributional Reinforcement Learning

Linear Estimation in Interconnected Sensor Systems with Information Constraints

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A Survey on Policy Search for Robotics

A Survey on Policy Search for Robotics

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