Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems

Schahin Tofangchi

ISBN 10: 3736972008 / ISBN 13: 9783736972001

Published by Cuvillier, 2020

About this Item

Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems | Schahin Tofangchi | Taschenbuch | Göttinger Wirtschaftsinformatik | 202 S. | Englisch | 2020 | Cuvillier | EAN 9783736972001 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 118354312

Bibliographic Details

• Title: Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems
• Publisher: Cuvillier
• Publication Date: 2020
• Language: English
• ISBN 10: 3736972008
• ISBN 13: 9783736972001
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 12D millimeters 210 millimeters width by 148 millimeters height by 12 millimeters depth
• Weight: 269 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.