Programming Collective Intelligence

This book, with machine learning and computational statistics as the background of the topic, specializes in how to mine and analyze data and resources on the Web, and how to analyze user experience, marketing, personal tastes, and many other pieces of information and draw useful conclusions by means of sophisticated algorithms to acquire, collect, and analyze user data and feedback from Web sites in order to create new user value and business value.

This informative book includes collaborative filtering techniques (to achieve the function of related product recommendation), cluster data analysis (to discover similar data subsets in large-scale data sets), core search engine techniques (crawlers, indexes, query engines, PageRank algorithms, etc.), optimization algorithms for searching massive amounts of information and analyzing statistics to draw conclusions, Bayesian filtering (spam filtering, text filtering), and decision-making with decision-making algorithms (e.g., the use of spam and text filtering). filtering, text filtering), predictive and decision modeling functions with decision tree techniques, information matching techniques for social networks, machine learning and artificial intelligence applications. This book is an excellent choice for Web developers, architects, application engineers, and more.

Machine Learning for Hackers

Machine Learning for Hackers (Chinese translation: Machine Learning - Practical Case Analysis) explains machine learning algorithms through examples, implemented in R, you can learn machine learning while learning R. This is a hands-on book, focusing on how to use R to do data mining. Put on how to use R to do data mining, machine learning algorithms more through the black box way to speak, emphasizing the meaning of input, output, weakening the details of machine learning algorithms. The text is basically through the case to tell how to solve the problem, and provide raw data for their own analysis. It is suitable for two kinds of people:

(1) some theories of machine learning, lack of case practice

(2) only need to master how to solve problems with generalized machine learning, only want to know the general idea of machine learning algorithms, don't want to learn the algorithms of machine learning in detail.

Machine Learning by Tom M Mitchell

Machine Learning demonstrates the algorithms and theories at the heart of machine learning and clarifies how the algorithms work. Machine Learning synthesizes many findings, such as statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity, and cybernetics, and uses them to understand the context of the problem, the algorithms, and the implicit assumptions made therein.

The Elements of Statistical Learning

The Elements of Statistical Learning introduces some of the key concepts in these areas. Although statistical methods are applied, the emphasis is on concepts rather than mathematics. Many examples are accompanied by color illustrations. The Elements of Statistical Learning covers a wide range of topics, from guided learning (prediction) to unguided learning. Including topics such as neural networks, support vector machines, classification trees, and boosting, it is the most comprehensive introduction of its kind.

The rapid advances in computing and information technology have brought with them vast amounts of data in many fields, including medicine, biology, finance, and marketing. Making sense of this data is a challenge that has led to the development of new tools in the field of statistics, extending into new areas such as data mining, machine learning, and bioinformatics. Many of the tools have the *** same basis, but are often expressed in different terms.

Learning from Data

This is an introductory course in Machine Learning (ML), covering its basic theory, algorithms, and applications. Machine learning is a key technology for big data and applications in finance, medicine, business and research. Machine learning enables computing systems to automatically learn how to perform targeted tasks with information extracted from data. Machine Learning is now one of the hottest research areas of the day and is a training course for undergraduate and graduate students in 15 different majors at Caltech. This course maintains a balance between theory and practice, and covers mathematical and heuristic methods.

Pattern Recognition and Machine Learning

This book is one of the godsends of machine learning, a must-read classic!

Artificial Intelligence

Artificial Intelligence: A Modern Approach

With detailed and informative material on rational intelligences, Artificial Intelligence: A Modern Approach provides a comprehensive account of the the core of the field of artificial intelligence and provides an in-depth introduction to each of the major research directions, making it a rare and comprehensive textbook.

Artificial Intelligence for Humans

This book elucidates basic AI algorithms such as dimensionality, distance metrics, clustering, error computation, and linear regression, using a rich variety of case studies for illustration. A good foundation in math is required.

Paradigm of Artificial Intelligence Programming

This book introduces excellent programming paradigms and basic AI theory, and is a must-read for little ones working in the field of AI.

Artificial Intelligence: a New Synthesis

This book presents a new integrated approach to unifying AI theories, covering such things as neural networks, computer vision, heuristic search, Bayesian networks, and more. A must-read for advanced players.

The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of Human Mind

In this mind-bending book, tech pioneer Marvin Minsky continues his highly inventive research to give us a new and incredible look at how the human brain works.

Artificial Intelligence (3rd Edition)

This is a primer on artificial intelligence. The explanations and concepts can be easily understood by people with no programming knowledge. It is simplified, but also contains a high level exploration of the field of artificial intelligence.