John Winn
Table of Contents
#CHAPTER-NAV#

Table of Contents

Preface
Introduction to the book, who it is for and how to read it.
How can machine learning solve my problem?
What model-based machine learning is and how it helps solve problems.
1. A Murder Mystery
Introduces all the essential concepts of model-based machine learning, in the course of solving a murder.
Key concepts: probability, random variable, probabilistic inference, probabilistic model, factor graph, Bayes' theorem.
2. Assessing People's Skills
A first application of model-based machine learning: assessing what skills a person has based on their answers in a test.
Key concepts: message passing algorithm, loopy belief propagation, visualisation, evaluation metric, ROC curve.
Interlude: the machine learning life cycle
The typical steps in solving any machine learning problem.
3. Meeting Your Match
A real-world application of model-based machine learning to the problem of matching players in online games.
Key concepts: Gaussian distribution, variance, conjugate distribution, expectation propagation, online learning.
4. Uncluttering Your Inbox
A model that removes the clutter from a user's inbox by learning which emails they are likely to ignore.
Key concepts: overfitting, anonymisation, classification, feature set, precision-recall curve, cold start problem.
5. Making Recommendations
Learning a model of people and movies, so they can be matched together to make useful recommendations.
Key concepts: collaborative filtering, symmetries, symmetry breaking.
6. Understanding Asthma
Modelling the way children acquire allergies, to understand and predict childhood asthma.
Key concepts: time series, missing data, model selection, model evidence, Occam's razor, gate, discrete distribution.
7. Harnessing the Crowd
A model that uses crowd-sourced labels to provide accurate information in crisis situations.
Key concepts: Dirichlet distribution, confusion matrix, naive Bayes classifier.
8. How to Read a Model
Exploring models created by other people to understand the assumptions they are making.
Key concepts: Latent Dirichlet Allocation, decision tree, principal component analysis, k-means clustering.
Afterword
Some final thoughts on the future of model-based machine learning.