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A First Course in Machine Learning 1st Edition by Simon Rogers, Mark Girolami ISBN 1439824142 9781439824146

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A First Course in Machine Learning 1st Edition Simon Rogers Digital Instant Download

Author(s): Simon Rogers, Mark Girolami
ISBN(s): 9781439824146, 1439824142
Edition: 1
File Details: PDF, 7.13 MB
Year: 2011
Language: english
SKU: EB-4982612 Category: Tags: ,

A First Course in Machine Learning 1st Edition by Simon Rogers, Mark Girolami – Ebook PDF Instant Download/Delivery: 1439824142, 9781439824146
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ISBN 10: 1439824142 
ISBN 13: 9781439824146
Author: Simon Rogers, Mark Girolami

A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website ( an extensive collection of MATLAB ® /Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail.

A First Course in Machine Learning 1st Table of contents:

Part I: Basic Topics

Linear Modelling: A Least Squares Approach
Linear Modelling: A Maximum Likelihood Approach
The Bayesian Approach to Machine Learning
Bayesian Inference
Part II: Core Machine Learning Techniques

Classification
Clustering
Principal Components Analysis and Latent Variable Models
Part III: Advanced Topics

Further Topics in Markov Chain Monte Carlo
Classification and Regression with Gaussian Processes
Dirichlet Process Models

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