How to design programs : an introduction to programming and computing / / Matthias Felleisen ... [et al.]

• Record Nr.: UNINA9910146960503321
• Pubbl/distr/stampa: Cambridge, Massachusetts : , : MIT Press, , c2001; [Piscataqay, New Jersey] : , : IEEE Xplore, , [2001]
• ISBN: 0-262-30019-2; 9786612096303; 1-282-09630-3; 0-262-25611-8; 0-585-39296-X
• Descrizione fisica: 1 online resource (xxx, 693 p. ) : ill.
• Altri autori (Persone): FelleisenMatthias
• Disciplina: 005.1/2
• Soggetti: Computer programming; Electronic data processing; Engineering & Applied Sciences; Computer Science
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references and index.
• Sommario/riassunto: This introduction to programming places computer science in the core of a liberal arts education. Unlike other introductory books, it focuses on the program design process. This approach fosters a variety of skills--critical reading, analytical thinking, creative synthesis, and attention to detail--that are important for everyone, not just future computer programmers.The book exposes readers to two fundamentally new ideas. First, it presents program design guidelines that show the reader how to analyze a problem statement; how to formulate concise goals; how to make up examples; how to develop an outline of the solution, based on the analysis; how to finish the program; and how to test. Each step produces a well-defined intermediate product. Second, the book comes with a novel programming environment, the first one explicitly designed for ; beginners. The environment grows with the readers as they master the material in the book until it supports a full-fledged language for the whole spectrum of programming tasks.All the book's support materials are available for free on the Web. The Web site includes the environment, teacher guides, exercises for all levels, solutions, and additional projects.

Metalearning : Applications to Automated Machine Learning and Data Mining

• Record Nr.: UNINA9910548277503321
• Autore: Brazdil Pavel
• Pubbl/distr/stampa: Cham, : Springer Nature, 2022; Cham : , : Springer International Publishing AG, , 2022; ©2022
• ISBN: 3-030-67024-4
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (349 pages)
• Collana: Cognitive Technologies
• Classificazione: COM004000COM021030
• Altri autori (Persone): van RijnJan N; SoaresCarlos; VanschorenJoaquin
• Disciplina: 006.31
• Soggetti: Artificial intelligence; Data mining; Machine learning; Aprenentatge automàtic; Mineria de dades; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from ; past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.