Machine learning and deep learning

By Christian Janiesch, Patrick Zschech, Kai Heinrich. Electronic Markets, 2021

1,800 citations recorded by the source corpus.

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

Subfields: Information Systems, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a0f303b6e22ef52943355993f57d65938997066a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Gero Strobel
2. Patrick Zschech
3. Leonardo Banh
4. Jochen Hartmann
5. Christian Janiesch
6. Stefan Feuerriegel

In Management Information Systems

1. Stefan Feuerriegel

In Management Science and Operations Research

1. Gero Strobel
2. Leonardo Banh
3. Stefan Feuerriegel
4. Longbing Cao
5. Dogu Araci

Citations in the corpus

• Generative artificial intelligence (2023, background): “Supervised learning is a common approach, e.g., for applications in commercial contexts, as algorithms are trained on labeled datasets to classify or forecast (business) data (Janiesch et al., 2021).”
• Generative AI (2023, positive): “Deep neural networks are particularly well suited for the purpose of data generation, especially as deep neural networks can be designed using different architectures to model different data types (Janiesch et al., 2021; Kraus et al., 2020), for example, sequential data such as human language or spa”