A rich and important area for the applications of linear algebra is machine learning. In machine learning, one aims to achieve optimized or learned understanding of various kinds of real-world phenomena from data collected or observed, without real comprehension of the functioning mechanisms of such phenomena. These functioning mechanisms are often impossible or unpractical to grasp anyway. In this chapter, we present several introductory and fundamental problems in supervised machine learning including linear regression, data classification, and logistic regression and the mathematical and computational methods associated.

Chapter two traces the SED’s institutionalization of environmental protection and the party’s conviction that socialism provided solutions to pollution. The SED succeeded in creating a more environmentally minded population and, at least initially, tried to address concerns within existing structures. The SED used mass social campaigns to unite East Germans around the issue of environmentalism and practiced protection through policy and negotiating petitions. The GDR simultaneously reached out to other socialist countries to build coalitions around its brand of environmentalism in contrast to the one taking off in western Europe. This positioning intentionally placed the GDR in the middle of a regional and global phenomenon that spanned the Iron Curtain. Despite minor improvements, however, the discrepancy between rhetoric and lived reality produced a politically untenable situation. The SED relied on the Stasi to police the population, and ultimately opted to classify all environmental data in 1982.