FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title: Gaussian Processes for Machine Learning
 Author(s): Carl Edward Rasmussen, Christopher K. I. Williams
 Publisher: The MIT Press (November 23, 2005)
 Permission: "The MIT Press have kindly agreed to allow us to make the book available on the web."
 Hardcover/Paperback: 266 pages
 eBook: PDF Files
 Language: English
 ISBN10: 026218253X
 ISBN13: 9780262182539
 Share This:
Book Description
Gaussian Processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machinelearning community over the past decade, and this book provides a longneeded systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and selfcontained, targeted at researchers and students in machine learning and applied statistics.
The book deals with the supervisedlearning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed.
The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
About the Authors Carl Edward Rasmussen is a Lecturer at the Department of Engineering, University of Cambridge, and Adjunct Research Scientist at the Max Planck Institute for Biological Cybernetics, TÃ¼bingen.
 Machine Learning
 Probability and Stochastic Processes
 Statistics and SAS Programming
 Artificial Intelligence
 Data Analysis and Data Mining
 Neural Networks

Pattern Recognition and Machine Learning (Christopher Bishop)
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

Bayesian Reasoning and Machine Learning (David Barber)
This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problemsolving skills that equip them for the real world. Numerous examples and exercises are provided.

Distributional Reinforcement Learning (Marc G. Bellemare, et al)
Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.

Machine Learning under Resource Constraints (Katharina Morik)
It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

An Introduction to Machine Learning Interpretability
Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

Hyperparameter Tuning for Machine Learning: A Practical Guide
This open access book provides a wealth of handson examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

Approaching (Almost) Any Machine Learning Problem
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

An Introduction to Quantum Machine Learning for Engineers
This book provides a selfcontained introduction to Quantum Machine Learning for an audience of engineers with a background in probability and linear algebra, describes the necessary background, concepts, and tools, covers parametrized quantum circuits, etc.

Mathematics for Machine Learning (Marc P. Deisenroth, et al.)
This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

Metalearning: Applications to Automated Machine Learning
This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.

Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudocode for the most important algorithms.

Probabilistic Machine Learning: Advanced Topics (Kevin Murphy)
This book expands the scope of Machine Learning to encompass more challenging problems, discusses methods for discovering 'insights' about data, and how to use probabilistic models for causal inference and decision making under uncertainty.

The Big Book of Machine Learning Use Cases
This howto reference guide provides everything you need  including code samples and notebooks  to start putting Machine Learning to work. It's a collection of technical blogs from industry thought leaders with practical use cases you can leverage today.

Introduction to Online Convex Optimization (Elad Hazan)
This book presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed.

Convex Optimization for Machine Learning (Changho Suh)
This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal is to help develop a sense of what convex optimization is, and how it can be used.

Pen and Paper Exercises in Machine Learning (Michael Gutmann)
This is a collection of (mostly) penandpaper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.

Interpretable Machine Learning: Black Box Models Explainable
This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.

Machine Learning with Neural Networks (Bernhard Mehlig)
This modern and selfcontained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

Machine Learning from Scratch (Danny Friedman)
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.

Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.
:






















