A First Course in Machine Learning

A First Course in Machine Learning

by Simon Rogers, Mark Girolami
     
 

ISBN-10: 1439824142

ISBN-13: 9781439824146

Pub. Date: 11/15/2011

Publisher: Taylor & Francis

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 learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small

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Overview

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 learning: 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 (http://bit.ly/firstcourseml), 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.

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Product Details

ISBN-13:
9781439824146
Publisher:
Taylor & Francis
Publication date:
11/15/2011
Edition description:
New Edition
Pages:
305
Product dimensions:
6.00(w) x 8.90(h) x 0.70(d)

Table of Contents

Linear Modelling: A Least Squares Approach
Linear modelling
Making predictions
Vector/matrix notation
Nonlinear response from a linear model
Generalisation and over-fitting
Regularised least squares

Linear Modelling: A Maximum Likelihood Approach
Errors as noise
Random variables and probability
Popular discrete distributions
Continuous random variables — density functions
Popular continuous density functions
Thinking generatively
Likelihood
The bias-variance tradeoff
Effect of noise on parameter estimates
Variability in predictions

The Bayesian Approach to Machine Learning
A coin game
The exact posterior
The three scenarios
Marginal likelihoods
Hyper-parameters
Graphical models
A Bayesian treatment of the Olympics 100 m data
Marginal likelihood for polynomial model order selection
Summary

Bayesian Inference
Nonconjugate models
Binary responses
A point estimate — the MAP solution
The Laplace approximation
Sampling techniques
Summary

Classification
The general problem
Probabilistic classifiers
Nonprobabilistic classifiers
Assessing classification performance
Discriminative and generative classifiers
Summary

Clustering
The general problem
K-means clustering
Mixture models
Summary

Principal Components Analysis and Latent Variable Models
The general problem
Principal components analysis (PCA)
Latent variable models
Variational Bayes
A probabilistic model for PCA
Missing values
Non-real-valued data
Summary

Glossary

Index

Exercises and Further Reading appear at the end of each chapter.

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