Type:

Bachelor Thesis Business Information Systems

Status:

in process

Tutor:

• Pierre Maier

Abstract

Motivation

Currently, arguably no technology is as much in the spotlight as machine learning. It is regarded as the game changer by many – and as an enabler of a bright future by some: "And as a result of all this, our lives will be longer, happier, and more productive." (Domingos) Unfortunately, continuing the inglorious tradition of AI research in general, machine learning of often presented as a mixture of mystification and truly impressive results. This master thesis is to contribute to demystification, of machine learning in general, and of its effect on automation in business firms in particular. To this end, it is at first required to develop a critical overview of current approaches to machine learning. Subsequently it needs to be analysed how to frame problems that cannot be automated with traditional approaches to software engineering, but that could be successfully approached by machine learning. The thesis should finally produce a framework that includes classes of managerial problems in business firms that are cases for the successful application of machine learning.

Literature

Brynjolfsson, E., & McAfee, A. (op. 2014). The second machine age: Work, progress, and prosperity in the time of brilliant technologies. New York: W. W. Norton & Company.

Domingos, P. (2017). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. London: Penguin Books Ltd.

Murphy, K. P. (2012). Machine learning: A probabilistic perspective. Adaptive computation and machine learning series: MIT Press.