Collective wisdom of programming
Based on the theme of machine learning and computational statistics, this book specifically tells how to mine and analyze data and resources on the Web, how to analyze user experience, marketing, personal taste and many other information, and draws useful conclusions. Through complex algorithms, we can obtain, collect and analyze user data and feedback information from websites, thus creating new user value and commercial value.
The book is informative, including collaborative filtering technology (realizing the recommendation function of related products), clustering data analysis (finding similar data subsets in large-scale data sets), and core technologies of search engines (crawler, index, query engine, PageRank algorithm, etc.). ), optimization algorithm for searching massive information and making analysis and statistics, Bayesian filtering technology (spam filtering and text filtering), prediction and decision modeling with decision tree technology, and information matching technology of social networks. This book is an excellent choice for Web developers, architects and application engineers.
Machine learning of hackers
Machine learning of hackers (Chinese translation: machine learning-practical case analysis) explains the machine learning algorithm through examples, which is realized by R. You can learn R while learning machine learning. This is a very practical book, focusing on how to use R to do data mining. The algorithm of machine learning emphasizes the meaning of input and output and weakens the details of machine learning algorithm by means of black box. This paper basically uses cases to tell how to solve problems and provide original data for their own analysis. Suitable for two kinds of people:
(1) There have been some theories of machine learning, but there are few case exercises.
(2) People who only need to master how to solve problems with general machine learning only want to know the general idea of machine learning algorithms and don't want to learn the algorithms in machine learning in detail.
Tom Mitchell's Machine Learning
Machine learning shows the core algorithm and theory in machine learning, and expounds the running process of the algorithm. Machine learning combines many research results, such as statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and cybernetics. To understand the background, algorithm and implicit assumptions of the problem. Machine learning can be used as a teaching material for undergraduate and graduate students majoring in computer science, and also as a reference book for researchers and teachers in related fields.
Elements of statistical learning
Elements of Statistical Learning introduces some important concepts in these fields. Although statistical methods are applied, concepts are emphasized, not mathematics. Many examples are accompanied by color pictures. The content of statistical learning is very extensive, from supervised learning (prediction) to unsupervised learning. Including neural network, support vector machine, classification tree, promotion and other topics, it is the most comprehensive introduction of its kind.
The rapid development of computing and information technology has brought massive data to many fields, such as medicine, biology, finance and marketing. Understanding these data is a challenge, which leads to the development of new tools in the field of statistics and extends to new fields such as data mining, machine learning and bioinformatics. Many tools have the same foundation, but they are often expressed in different terms.
Learn from the data
This is an introductory course of machine learning (ML), which covers its basic theory, algorithm and application. Machine learning is the key technology of big data and its application in finance, medicine, business, scientific research and other fields. Machine learning enables computing systems to automatically learn how to perform target tasks through information extracted from data. Machine learning has become one of the hottest research fields at present, and it is also a training course for undergraduate and graduate students of different majors in California Institute of Technology 15. This course keeps a balance between theory and practice, covering mathematics and heuristic methods.
Pattern recognition and machine learning
This book is one of the masterpieces of machine learning and a must-read classic!
artificial intelligence
Artificial intelligence: a modern method
"Artificial Intelligence: A Modern Method", with rich and detailed materials, comprehensively expounds the core content in the field of artificial intelligence from the perspective of rational subjects, and deeply introduces the main research directions. It is a rare comprehensive textbook.
Human artificial intelligence
This book explains basic artificial intelligence algorithms, such as size, distance measurement, clustering, error calculation and linear regression. , and explained with rich cases. Need a good mathematical foundation.
Artificial intelligence programming paradigm
This book introduces excellent programming paradigm and basic AI theory, and is a must-read for friends who are committed to the field of artificial intelligence.
Artificial Intelligence: A New Synthesis
This book puts forward a new integration method to unify the theory of artificial intelligence, covering neural network, computer vision, heuristic search, Bayesian network and so on. Advanced players must read.
Emotional machine: the future of common sense thinking, artificial intelligence and human thinking
In this eye-opening book, Marvin Minsky, a pioneer in science and technology, continues his creative research and presents us with a brand-new and incredible operating mode of the human brain.
Artificial Intelligence (3rd Edition)
This is an introductory book about artificial intelligence. People who have no programming foundation can easily understand the explanations and concepts. Simplify the complex, but it also includes discussions in the field of advanced artificial intelligence.