Machine Learning

Gaussian Processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.

This book is a primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. It focuses on just a few powerful models (algorithms) that are extremely effective on real problems, rather than presenting a broad survey of machine learning algorithms as many books do.

This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.

This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.

The world of machine learning is evolving so quickly that it's challenging to find real-world use cases that are relevant to what you're working on.

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.

This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

This book delves into the fundamental mathematical concepts that underpin the field of machine learning, providing a comprehensive exploration of the mathematical principles behind algorithms and models.

Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

This monograph provides a self-contained introduction to Quantum Machine Learning for an audience of engineers with a background in probability and linear algebra. It first describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.

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 doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics.

This open access book provides a wealth of hands-on 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.

This book helps beginners learn the art and science of machine learning, presens real-world examples that leverage the popular Python machine learning ecosystem. You will receive a complete understanding of machine learning fundamentals.

This practical guide provides helps to solve machine learning challenges you may encounter in your work. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

This book is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.