rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: MATLAB machine learning recipes
    sub title: a problem-solution approach
  name:
    name part: Paluszek, Michael
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Thomas, Stephanie
    name part: (Educator)
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: nyu
    date issued: 2019
    copyright date: 2019
    edition: Second edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. You will: Learn to write code for machine learning, adaptive control and estimation using MATLAB See how these three areas complement each other Understand why these three areas are needed for robust machine learning applications Use MATLAB graphics and visualization tools for machine learning Code real world examples in MATLAB for major applications of machine learning in big data.
  table of contents: Introduction -- An overview of machine learning -- Representation of data for machine learning in MATLAB -- MATLAB graphics -- Kalman filters -- Adaptive control -- Fuzzy logic -- Data classification with decision trees -- Introduction to neural nets -- Classification of numbers using neural networks -- Pattern recognition with deep learning -- Neural aircraft control -- Multiple hypothesis testing -- Autonomous driving with multiple hypothesis testing -- Case-based expert systems -- A brief history of autonomous learning -- Software for machine learning.
  note: Michael Paluszek and Stephanie Thomas.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  note: Includes bibliographical references and index.
  subject:
    title info:
      title: MATLAB
  subject:
    title info:
      title: MATLAB
  subject:
    topic: Machine learning
  identifier: 9781484239162
  identifier: 1484239164
  identifier: 9781484252413
  identifier: 1484252411
  identifier: 9781484239155
  identifier: 1484239156
  identifier: 9781484239179
  identifier: 1484239172
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: N$T
    record creation date: 190201
    record change date: 20240717213016.0
    record identifier: kn-on1083763032
    language of cataloging:
      language term: eng