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:
    non sort: A
    title: first course in machine learning
  name:
    name part: Rogers, Simon
    name part: 1979-
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: Girolami, Mark
    name part: 1963-
    role:
      role term: author
  type of resource: text
  origin info:
    place:
      place term: flu
    place:
      place term: Boca Raton, Florida
    publisher: CRC Press
    date issued: c2012
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xx, 285 p. : ill. ; 25 cm.
  abstract: "Machine Learning is rapidly becoming one of the most important areas of general practice, research and development activity within Computing Sci- ence. This is re ected in the scale of the academic research area devoted to the subject and the active recruitment of Machine Learning specialists by major international banks and nancial institutions as well as companies such as Microsoft, Google, Yahoo and Amazon. This growth can be partly explained by the increase in the quantity and diversity of measurements we are able to make of the world. A particularly fascinating example arises from the wave of new biological measurement technologies that have preceded the sequencing of the first genomes. It is now possible to measure the detailed molecular state of an organism in manners that would have been hard to imagine only a short time ago. Such measurements go far beyond our understanding of these organisms and Machine Learning techniques have been heavily involved in the distillation of useful structure from them. This book is based on material taught on a Machine Learning course in the School of Computing Science at the University of Glasgow, UK. The course, presented to nal year undergraduates and taught postgraduates, is made up of 20 hour-long lectures and 10 hour-long laboratory sessions. In such a short teaching period, it is impossible to cover more than a small fraction of the material that now comes under the banner of Machine Learning. Our inten- tion when teaching this course therefore, is to present the core mathematical and statistical techniques required to understand some of the most popular Machine Learning algorithms and then present a few of these algorithms that span the main problem areas within Machine Learning: classi cation, clus- tering"-- Provided by publisher
  table of contents: 1. Linear modelling: a least squares approach -- 2. Linear modelling: a maximum likelihood approach -- 3. The bayesian approach to machine learning -- 4. Bayesian inference -- 5. Classification -- 6. Clustering -- 7. Principal components analysis and latent variable models
  note: Simon Rogers, Mark Girolami
  note: Includes bibliographical references and index
  subject:
    topic: Machine learning
  classification: Q325.5 .R64 2012
  identifier: 1439824142 (hardcover : alk, paper)
  identifier: 9781439824146 (hardcover : alk, paper)
  record info:
    record creation date: 120628
    record change date: 20130225093000.0
    record identifier: 0000079083