Titlebook: Machine Learning Foundations; Supervised, Unsuperv Taeho Jo Book 2021 Springer Nature Switzerland AG 2021 Machine Learning.Supervised Learn

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Data Encodingsts of records is the typical raw data type, and it is relatively easy to encode them into numerical vectors. We mention the textual data as the most popular raw data type in the real world and study the process of indexing a text into a list of words and encoding it into a numerical vector. We also

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Simple Machine Learning Algorithms, it is assumed that the given task is a binary classification, and the regression or the multiple classification may be decomposed into binary classifications. Some simple machine learning algorithms, which are given as threshold rules or hypercubes, will be mentioned for helping understanding the

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Probabilistic Learningobability theory, which is called Bayes Theorem, in order to provide the background for understanding the chapter. We describe in detail some probabilistic classifiers such as Bayes Classifier and Naive Bayes as the popular and simple machine learning algorithms. We cover the Bayesian Learning as th

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Support Vector Machiner classifier and the basis for deriving the SVM. In the main part, we cover the classification process, the constraints, and the learning process of the SVM. We survey some variants of the SVM that are expansions of the standard SVM. The SVM is applicable to a nonlinear classification problem, robus

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K Means Algorithme KNN algorithm. With respect to the clustering process, we study the two main versions of the k means algorithm: the crisp k means algorithm and the fuzzy k means algorithm. The k medoid algorithm is mentioned as a variant of the k means algorithm, and the strategies of selecting representative ite

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