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Simon Rogers Mark Girol A First Course in Machine Learn (Livre de poche) (IMPORTATION BRITANNIQUE)

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Numéro de l'objet eBay :276271719641
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Caractéristiques de l'objet

État
Neuf: Livre neuf, n'ayant jamais été lu ni utilisé, en parfait état, sans pages manquantes ni ...
Format
Trade Paperback
Book Title
A First Course in Machine Learning
Publication Name
First Course in Machine Learning
Title
A First Course in Machine Learning
EAN
9780367574642
ISBN
9780367574642
Publisher
CRC Press LLC
Release Year
2020
Release Date
30/06/2020
Language
English
Country/Region of Manufacture
GB
Item Height
234mm
Item Length
9.2 in
Item Weight
26.2 Oz
Author
Mark Girolami, Simon Rogers
Genre
Business & Finance
Subject
Programming / Games
Series
Chapman and Hall/Crc Machine Learning and Pattern Recognition Ser.
Subject Area
Computers
Publication Year
2020
Type
Textbook
Item Width
6.1 in
Number of Pages
428 Pages

À propos de ce produit

Product Information

The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.

Product Identifiers

Publisher
CRC Press LLC
ISBN-10
0367574640
ISBN-13
9780367574642
eBay Product ID (ePID)
27064611596

Product Key Features

Author
Mark Girolami, Simon Rogers
Publication Name
First Course in Machine Learning
Format
Trade Paperback
Language
English
Subject
Programming / Games
Series
Chapman and Hall/Crc Machine Learning and Pattern Recognition Ser.
Publication Year
2020
Type
Textbook
Subject Area
Computers
Number of Pages
428 Pages

Dimensions

Item Length
9.2 in
Item Width
6.1 in
Item Weight
26.2 Oz

Additional Product Features

Edition Number
2
Lc Classification Number
Q325.5.R64 2020
Reviews
"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength. While there are other books available that aim for completeness, with exhaustively comprehensive introductions to every branch of machine learning, the book by Rogers and Girolami starts with the basics, builds a solid and logical foundation of methodology, before introducing some more advanced topics. The essentials of the model construction, validation, and evaluation process are communicated clearly and in such a manner as to be accessible to the student taking such a course. I was also pleased to see that the authors have not shied away from producing algebraic derivations throughout, which are for many students an essential part of the learning process--many other texts omit such details, leaving them as 'an exercise for the reader.' Being shown the explicit steps required for such derivations is an important part of developing a sense of confidence in the student. Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." --David Clifton, University of Oxford, UK "In my opinion, this is by far the best introduction to Machine Learning. It accomplishes something I would think impossible: it assumes essentially only high school mathematics and no statistics background, and yet, by introducing math, probability and statistics as needed, it manages to do an entirely rigorous introduction to Machine Learning. Proofs are not provided only for very few theorems; the book goes fairly deep and is really enjoyable to read. I told my students that this book will be one of the best investments they have ever made!" --Aleksandar Ignjatovic, University of New South Wales "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduct
Table of Content
Linear Modelling: A Least Squares Approach. Linear Modelling: A Maximum Likelihood Approach. The Bayesian Approach to Machine Learning. Bayesian Inference. Classification. Clustering. Principal Components Analysis and Latent Variable Models. Further Topics in Markov Chain Monte Carlo. Classification and Regression with Gaussian Processes. Dirichlet Process models.
Copyright Date
2017
Dewey Decimal
006.31
Dewey Edition
22
Illustrated
Yes

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RAREWAVES. COM LIMITED
Unit 145, The Light Box
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Numéro de TVA :
  • GB 864 1548 11
Numéro d'immatriculation de la société :
  • 05540102
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