Machine Learning: An Algorithmic Perspective

Machine Learning: An Algorithmic Perspective

by Stephen Marsland
     
 

Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also

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Overview

Traditional books on machine learning can be divided into two groups — those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.

Theory Backed up by Practical Examples

The book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.

Highlights a Range of Disciplines and Applications

Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

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Product Details

ISBN-13:
9781420067187
Publisher:
Taylor & Francis
Publication date:
04/13/2009
Edition description:
Older Edition
Pages:
406
Sales rank:
1,304,318
Product dimensions:
6.30(w) x 9.30(h) x 1.00(d)

Table of Contents

Introduction

If Data Had Mass, The Earth Would Be a Black Hole

Learning

Types of Machine Learning

Supervised Learning

The Brain and the Neuron

Linear Discriminants

Preliminaries

The Perceptron

Linear Separability

Linear Regression

The Multi-Layer Perceptron

Going Forwards

Going Backwards: Back-propagation of Error

The Multi-Layer Perceptron in Practice

Examples of Using the MLP

Overview

Back-propagation Properly

Radial Basis Functions and Splines

Concepts

The Radial Basis Function (RBF) Network

The Curse of Dimensionality

Interpolation and Basis Functions

Support Vector Machines

Optimal Separation

Kernels

Learning With Trees

Using Decision Trees

Constructing Decision Trees

Classification And Regression Trees (CART)

Classification Example

Decision by Committee: Ensemble Learning

Boosting

Bagging

Different Ways to Combine Classifiers

Probability and Learning

Turning Data into Probabilities

Some Basic Statistics

Gaussian Mixture Models

Nearest Neighbour Methods

Unsupervised Learning

The k-Means Algorithm

Vector Quantisation

The Self-Organising Feature Map

Dimensionality Reduction

Linear Discriminant Analysis (LDA)

Principal Components Analysis (PCA)

Factor Analysis

Independent Components Analysis (ICA)

Locally Linear Embedding

Isomap

Optimisation and Search

Going Downhill

Least-Squares Optimisation

Conjugate Gradients

Search: Three Basic Approaches

Exploitation and Exploration

Simulated Annealing

Evolutionary Learning

The Genetic Algorithm (GA)

Generating Offspring: Genetic Operators

Using Genetic Algorithms

Genetic Programming

Combining Sampling with Evolutionary Learning

Reinforcement Learning

Overview

Example: Getting Lost

Markov Decision Processes

Values

Back On Holiday: Using Reinforcement Learning

The Difference Between Sarsa and Q-Learning

Uses of Reinforcement Learning

Markov Chain Monte Carlo (MCMC) Methods

Sampling

Monte Carlo or Bust

The Proposal Distribution

Markov Chain Monte Carlo

Graphical Models

Bayesian Networks

Markov Random Fields

Hidden Markov Models (HMM)

Tracking Methods

Python

Installing Python and Other Packages

Getting Started

Code Basics

Using NumPy and Matplotlib

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