Data Science An Introduction To Statistics And Machine Learning Matthias Plaue by Matthias Plaue 9783662678817, 3662678810 instant download after payment.

This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of Data Science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and Machine Learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.

In this chapter, we will deal with supervised machine learning. Supervised methods are based on the statistical evaluation of a sample where each observation comes with an already known assignment of a label that the algorithm is ultimately supposed to predict for yet unseen data. That sample is called the training dataset. Keeping with the image classification example, a training dataset would consist of a (large) number of photographs, each of which has been (manually) annotated with one of the labels: landscape, portrait, etc. Ideally, the learning algorithm is then able to recognize patterns that characterize and distinguish between landscape and portrait photographs. More concretely, these patterns are statistical variations of features. For digital photographs, the raw features are given by the color values of each pixel. From these statistical patterns, rules are generated that are able to categorize new, yet to be seen photos that were not contained in the training dataset. These rules are not explicitly specified by the programmer but are “learned” by the machine on the basis of the training dataset.