Titlebook: Probability and Statistics for Computer Science; David Forsyth Textbook 2018 Springer International Publishing AG 2018 Summarizing 1D data

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xplaining why it’s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors..•   A chapter deal978-3-319-87788-4978-3-319-64410-3

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Textbook 2018lacement, and the simplest hypothesis testing..•   Achapter dealing with classification, explaining why it’s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors..•   A chapter deal

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list of practical skills a student should have absorbed..All.This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods

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Random Variables and Expectationsthe amount of money you win or lose on a bet is a random variable. Now if you take the same bet repeatedly, you could wonder how much money will change hands in total, per bet. This yields a new and useful idea, the expected value of a random variable.

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Inferring Probability Models from Datand see five heads and five tails. To be able to estimate the probability of seeing heads on a future flip, you would need to fit a model. But once you had done so, you could also estimate the probability that five future flips would give you three heads and two tails, and so on.

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Clustering: Models of High Dimensional Datato assume that it consists of multiple blobs. To build models like this, we must determine which datapoints belong to which blob by collecting together data points that are close and forming blobs out of them.

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Looking at Relationshipshts or the weights of a population (as in Fig. 1.7). But I could say nothing about the relationship between the height and weight. In this chapter, we will look at methods to visualize and summarize the relationships between pairs of elements of a dataset.

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Learning to Classify free on the web, you would use a classifier to decide whether it was safe to run it (i.e. look at the program, and say yes or no according to some rule). As yet another example, credit card companies must decide whether a transaction is good or fraudulent.