Machine Learning in Strategy and Implementation

This chapter provides an overview of the concept of ML. This is followed by a discussion of the growing influence of ML and of the different forms of ML. A brief overview of deep learning is introduced, while the final part of the chapter explores ML in strategy and strategy implementation. It is apparent in the discussion that ML is a concept that addresses the question related to how to construct computers that are capable of improving automatically through experience. Over the years, ML has grown substantially. Its growth and progress could be attributed to online data availability and low-cost computation. Three forms of ML exist, namely, supervised learning, unsupervised learning and reinforcement learning. In addition, two types of learning exist, namely, deep learning (DL) and shallow learning (SL). SL would typically contain the single-layer neural networks, whereas DL would contain many layers of neural networks. DL requires more computation power for forward or backward optimisation while training, testing and eventually running these neural networks. DL often outperforms shallow ML methods, which often consist of single neural networks. Firms applying ML in Big Data Analytics would gain an advantage in the variety of factors affecting strategy and strategy implementation.