Books like Gaussian processes for machine learning by Carl Edward Rasmussen


Gaussian processes (GPs) are a powerful framework for kernel‑based machine‑learning. This book presents a thorough, self‑contained treatment of both the theoretical foundations and practical aspects of GPs, aimed at researchers and students in machine learning and applied statistics. Readers learn how to employ GPs for problems ranging from regression and classification to broader statistical modeling with stochastic processes.
First publish date: 2005
Genre: Technology & Computing/Artificial Intelligence
Authors: Carl Edward Rasmussen
★ ★ ★ ★ ★ 4.0 (1 community ratings)

Gaussian processes for machine learning by Carl Edward Rasmussen

How are these books recommended?

The books recommended for Gaussian processes for machine learning by Carl Edward Rasmussen are shaped by reader interaction. Votes on how closely books relate, user ratings, and community comments all help refine these recommendations and highlight books readers genuinely find similar in theme, ideas, and overall reading experience.


Have you read any of these books?
Your votes, ratings, and comments help improve recommendations and make it easier for other readers to discover books they’ll enjoy.

Books similar to Gaussian processes for machine learning (9 similar books)

The Elements of Statistical Learning by 12158869|Trevor Hastie book cover

📘 The Elements of Statistical Learning

The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

★★★★★★★★★★ 4.3 (3 ratings)
Similar? ✓ Yes 0 ✗ No 0
Deep Learning by 11775475|Ian Goodfellow book cover

📘 Deep Learning

Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

★★★★★★★★★★ 3.7 (3 ratings)
Similar? ✓ Yes 0 ✗ No 0
Probabilistic Graphical Models by 10966103|Daphne Koller book cover

📘 Probabilistic Graphical Models


★★★★★★★★★★ 4.0 (2 ratings)
Similar? ✓ Yes 0 ✗ No 0
KERNEL METHODS FOR PATTERN ANALYSIS by 10841690|JOHN SHAWE-TAYLOR book cover

📘 KERNEL METHODS FOR PATTERN ANALYSIS


★★★★★★★★★★ 5.0 (1 rating)
Similar? ✓ Yes 0 ✗ No 0
Pattern Recognition and Machine Learning by 8824929|Christopher M. Bishop book cover

📘 Pattern Recognition and Machine Learning


★★★★★★★★★★ 0.0 (0 ratings)
Similar? ✓ Yes 0 ✗ No 0
Bioinformatics by 8328148|Pierre Baldi book cover

📘 Bioinformatics

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.

★★★★★★★★★★ 0.0 (0 ratings)
Similar? ✓ Yes 0 ✗ No 0
Introduction to Statistical Learning by 7184749|Gareth James book cover

📘 Introduction to Statistical Learning


★★★★★★★★★★ 0.0 (0 ratings)
Similar? ✓ Yes 0 ✗ No 0
Bayesian reasoning and machine learning by 11367792|David Barber book cover

📘 Bayesian reasoning and machine learning

A practical guide to Bayesian methods in machine learning, tailored for undergraduates and graduate students with modest math backgrounds.

★★★★★★★★★★ 0.0 (0 ratings)
Similar? ✓ Yes 0 ✗ No 0
Advances in financial machine learning by 11882973|Marcos Mailoc López de Prado book cover

📘 Advances in financial machine learning

"Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance"--

★★★★★★★★★★ 0.0 (0 ratings)
Similar? ✓ Yes 0 ✗ No 0

Some Other Similar Books

Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
Reproducing Kernel Hilbert Spaces in Probability and Statistics by Atousa Chaganty
The Gaussian Process Machine Learning Algorithm by Carl Edward Rasmussen

Have a similar book in mind? Let others know!

Please login to submit books!