Machine Learning In Pure Mathematics And Theoretical Physics Vdoc
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Mathematics for Machine Learning
Author: Marc Peter Deisenroth
language: en
Publisher: Cambridge University Press
Release Date: 2020-04-23
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Doc-Humanity
However you view the present time, it is a new century, a new world, and also a new humanity - in fact, humanity is not something that was ever defined once and for all, but remains an open project. For several decades we have been witnessing a revolution. However, unlike the political and ideological revolutions that took place around the First World War, this is a technological and much more radical one that does not depend on people's beliefs, but rather on the tireless labour of machines. The rise of automation has brought about a revelation of something that had hitherto remained hidden in the workshops of homo faber. That is, there are very few functions, apart from consumption, where a machine cannot replace a human being, be these material or spiritual - machines need energy, but they can also do without it, whereas humans die if deprived of it, or one can imagine a machine producing symphonies, but not enjoying them. So while human beings are still needed, their roles and scopes have to be reconsidered. Workers may be superfluous, but humans are still needed, including those who until recently only recognised themselves as producers. The exclusion of workers from production does not discount humans being able to produce value in the form of consumption. Recognising this will enable us to conceive the "Webfare" - a new digital system that will teach us to find new names and new forms, more tolerance and room for traditional human needs. Above all, it will teach us how to transform the time given to us by automation into an opportunity for progress.
Understanding Machine Learning
Author: Shai Shalev-Shwartz
language: en
Publisher: Cambridge University Press
Release Date: 2014-05-19
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.