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.
Table of Contents
Chapter 1 Linear Modelling: A Least Squares Approach
Chapter 2 Linear Modelling: A Maximum Likelihood Approach
Chapter 3 The Bayesian Approach to Machine Learning
Chapter 4 Bayesian Inference
Chapter 5 Classification
Chapter 6 Clustering
Chapter 7 Principal Components Analysis and Latent Variable Models
- Title: A First Course in Machine Learning
- Author: Mark Girolami, Simon Rogers
- Length: 305 pages
- Edition: 1
- Language: English
- Publisher:Chapman and Hall/CRC
- Publication Date: 2011-10-25
- ISBN-10: 1439824142
- ISBN-13: 9781439824146
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