The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject
This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. ´´I find I learn computer science material best by sitting down and writing programs,¿ the author writes, and the book reflects this approach.
The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The book´s main aims are: * To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate * To introduce the main issues surrounding the design of intelligent agents * To introduce the main issues surrounding the design of a multi-agent society * To introduce a number of typical applications for agent technology After reading the book the student should understand: * The notion of an agent, how agents are distinct from other software paradigms (e.g. objects) and the characteristics of applications that lend themselves to agent-oriented software * The key issues associated with constructing agents capable of intelligent autonomous action and the main approaches taken to developing such agents * The key issues in designing societies of agents that can effectively cooperate in order to solve problems, including an understanding of the key types of multi-agent interactions possible in such systems * The main application areas of agent-based systems
An unimaginably vast amount of data is now generated by our on-line lives and businesses, At the same time, our ability to store, manage, analyse, and exploit this data is becoming ever more sophisticated. This Very Short Introduction maps out the technology, and also the range of possibilities, challenges, and ethical questions it raises.
This textbook provides an in-depth introduction to software design, with a focus on object-oriented design, and using the Java programming language. Its goal is to help readers learn software design by discovering the experience of the design process. To this end, a narrative is used that introduces each element of design know-how in context, and explores alternative solutions in that context. The narrative is supported by hundreds of code fragments and design diagrams. The first chapter is a general introduction to software design. The subsequent chapters cover design concepts and techniques, which are presented as a continuous narrative anchored in specific design problems. The design concepts and techniques covered include effective use of types and interfaces, encapsulation, composition, inheritance, design patterns, unit testing, and many more. A major emphasis is placed on coding and experimentation as a necessary complement to reading the text. To support this aspect of the learning process, a companion website with practice problems is provided, and three sample applications that capture numerous design decisions are included. Guidance on these sample applications is provided in a section called ´´Code Exploration´´ at the end of each chapter. Although the Java language is used as a means of conveying design-related ideas, the book´s main goal is to address concepts and techniques that are applicable in a host of technologies. This book is intended for readers who have a minimum of programming experience and want to move from writing small programs and scripts to tackling the development of larger systems. This audience naturally includes students in university-level computer science and software engineering programs. As the prerequisites to specific computing concepts are kept to a minimum, the content is also accessible to programmers without a primary training in computing. In a similar vein, understanding the code fragments requires only a minimal grasp of the language, such as would be taught in an introductory programming course.
Despite growing interest in the mathematical analysis of algorithms, basic information on methods and models has rarely been directly accessible to practitioners, researchers, or students. This book organizes and presents that knowledge, fully introducing today´s primary techniques for mathematically analyzing algorithms. Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematical and computer science material, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They focus on ´´average-case´´ or ´´probabilistic´´ analysis, while also covering tools for ´´worst case´´ or ´´complexity´´ analysis. Improvements in this edition include: * Upgraded figures and code * Newer style for presenting much of the text´s math * An all-new chapter on trees This book´s thorough, self-contained coverage will help readers appreciate the field´s challenges, prepare them for advanced results covered in Donald Knuth´s books, and provide the background they need to keep abreast of new research. Coverage includes: recurrences, generating functions, asymptotics, trees, strings, maps, sorting, tree search, string search, and hashing algorithms. Ideal for junior- or senior-level courses on mathematical analysis of algorithms, this book will also be useful in courses on discrete mathematics for computer scientists, and in introducing mathematics students to computer science principles related to algorithms and data structures. Product Description Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, Second Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematics and computer science, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They emphasize the mathematics needed to support scientific studies that can serve as the basis for predicting algorithm performance and for comparing different algorithms on the basis of performance. Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. Numerous examples are included throughout to illustrate applications to the analysis of algorithms that are playing a critical role in the evolution of our modern computational infrastructure. Improvements and additions in this new edition include Upgraded figures and code Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, Second Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Authors Robert Sedgewick and the late Philippe Flajolet emphasize the mathematics needed to support scientific studies that can serve as the basis for predicting algorithm performance and for comparing different algorithms on the basis of performance. Improvements and additions in this new edition include upgraded figures and code, an all-new chapter introducing analytic combinatorics, and simplified derivations via analytic combinatorics throughout. The book´s thorough, self-contained coverage will help readers appreciate the field´s challenges and prepare them for advanced study.
Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.
This concise, accessible text provides a thorough introduction to quantum computing - an exciting emergent field at the interface of the computer, engineering, mathematical and physical sciences. Aimed at advanced undergraduate and beginning graduate students in these disciplines, the text is technically detailed and is clearly illustrated throughout with diagrams and exercises. Some prior knowledge of linear algebra is assumed, including vector spaces and inner products. However, prior familiarity with topics such as tensor products and spectral decomposition is not required, as the necessary material is reviewed in the text.