Machine Learning: The Art and Science of Algorithms that by Peter Flach

By Peter Flach

As the most complete computing device studying texts round, this publication does justice to the field's exceptional richness, yet with no wasting sight of the unifying ideas. Peter Flach's transparent, example-based process starts by means of discussing how a unsolicited mail filter out works, which supplies a right away creation to computing device studying in motion, with at the very least technical fuss. Flach presents case reports of accelerating complexity and diversity with well-chosen examples and illustrations all through. He covers quite a lot of logical, geometric and statistical types and cutting-edge subject matters akin to matrix factorisation and ROC research. specific awareness is paid to the imperative position performed through beneficial properties. using tested terminology is balanced with the creation of recent and precious strategies, and summaries of proper historical past fabric are supplied with guidelines for revision if worthwhile. those positive aspects confirm computer studying will set a brand new general as an introductory textbook.

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Extra resources for Machine Learning: The Art and Science of Algorithms that Make Sense of Data

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Given an n-by-d matrix X representing n data points in d -dimensional space, we first calculate the centre of mass or mean vector μ by averaging each column. We then zero-centre the data set by subtracting −μ from each row, which corresponds to a translation. Next, we rotate the data such that as much variance (a measure of the data’s ‘spread’ in a certain direction) as possible is aligned with our coordinate axes; this can be achieved by a matrix transformation known as principal component analysis, about which you will learn more in Chapter 10.

A typical example of unsupervised learning of a predictive model occurs when 1. 1. An overview of different machine learning settings. The rows refer to whether the training data is labelled with a target variable, while the columns indicate whether the models learned are used to predict a target variable or rather describe the given data. we cluster data with the intention of using the clusters to assign class labels to new data. We will call this predictive clustering to distinguish it from the previous, descriptive form of clustering.

We then remove the covered examples of that class, and repeat the process. This is sometimes called a separate-and-conquer approach. An interesting aspect of logical models, which sets them aside from most geometric and probabilistic models, is that they can, to some extent, provide explanations for their predictions. For example, a prediction assigned by a decision tree could be explained by reading off the conditions that led to the prediction from root to leaf. The model itself can also easily be inspected by humans, which is why they are sometimes called declarative.

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