Browse Machine Learning

Practical Artificial Intelligence Programming in Java

his Open Content book covers AI programming techniques using Java. The latest version has a completed new chapter on statistical natural language processing and a new section on embedded expert systems, and a new chapter on spam detection.

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Information Theory, Inference, and Learning Algorithms

This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and linear algebra as taught in a first- or second year undergraduate course on mathematics for scientists and engineers. Conventional courses on information theory cover not only the beautiful theoretical ideas of […]

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Inductive Logic Programming

This book is an introduction to inductive logic programming (ILP), a research field at the intersection of machine learning and logic programming, which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs. The book extensively covers empirical inductive logic programming, one of the […]

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Introduction to Machine Learning

Learning, like intelligence, covers such a broad range of processes that it is difficult to define precisely. A dictionary definition includes phrases such as “to gain knowledge, or understanding of, or skill in, by study, instruction, or experience,” and “modification of a behavioral tendency by experience.” Zoologists and psychologists study learning in animals and humans. […]

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Machine Learning, Neural and Statistical Classification

The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. Before describing the contents, we first need to define what we mean by classification, give some background to the […]

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Gaussian Processes for Machine Learning

The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many scientific and industrial fields. […]

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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

The field of Statistics is constantly challenged by the problems that science and industry brings to its door. In the early days, these problems often came from agricultural and industrial experiments and were relatively small in scope. With the advent of computers and the information age, statistical problems have exploded both in size and complexity. […]

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Introduction to Machine Learning

During the next few lectures we will be looking at the inference from training data problem as a random process modeled by the joint probability distribution over input (measurements) and output (say class labels) variables. In general, estimating the underlying distribution is a daunting and unwieldy task, but there are a number of constraints or […]

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Bayesian Reasoning and Machine Learning

Machine Learning is the study of data-driven methods capable of mimicking, understanding and aiding human and biological information processing tasks. In this pursuit, many related issues arise such as how to compress data, interpret and process it. Often these methods are not necessarily directed to mimicking directly human processing but rather to enhance it, such […]

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A First Encounter with Machine Learning

Machine learning is a relatively recent discipline that emerged from the general field of artificial intelligence only quite recently. To build intelligent machines researchers realized that these machines should learn from and adapt to their environment. It is simply too costly and impractical to design intelligent systems by first gathering all the expert knowledge ourselves […]

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