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  • Machine learning : a probabilistic perspective

    • Authors: Kevin P. Murphy (2012)

  • The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics.
  • Book

  • Introduction to machine learning

    • Authors: Ethem Alpaydin (2014)

  • Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods.
  • Book

  • Introduction to the theory of computation

    • Authors: Michael Sipser (2012)

  • The number one choice for today's computational theory course, this revision continues the book's well-known, approachable style with timely revisions, additional practice, and more memorable examples in key areas. A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR grammars.
  • Book

  • Python Machine Learning

    • Authors: Sebastian Raschka (2015)

  • Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms * Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasets
  • Book

  • Operating system concepts

    • Authors: Abraham Silberschatz (2018)

  • It combines instruction on concepts with real-world applications so that students can understand the practical usage of the content. End-of-chapter problems, exercises, review questions, and programming exercises help to further reinforce important concepts. New interactive self-assessment problems are provided throughout the text to help students monitor their level of understanding and progress.
  • Book

  • Artificial Intelligence : A Modern Approach

    • Authors: Stuart Russell (2021)

  • The 4th Edition brings readers up to date on the latest technologies,presents concepts in a more unified manner, and offers new or expanded coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI.
  • Book

  • An Introduction to Machine Learning

    • Authors: Miroslav Kubat (2021)

  • This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning.
  • Book

  • Machine Learning with R: Expert techniques for predictive modeling

    • Authors: Brett Lantz (2019)

  • Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.
  • Book

  • An Introduction to Machine Learning

    • Authors: Miroslav Kubat (2015)

  • This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.