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This book provides a state-of-the-art overview of current Machine Learning research and its exploitation in various application areas. It has become apparent that the deep integration of Artificial Intelligence (AI) methods in products and services is essential for companies to stay competitive. The use of AI allows large volumes of data to be analyzed, patterns and trends to be identified, and well-founded decisions to be made on an informative basis. It also enables the optimization of workflows, the automation of processes and the development of new services, thus creating potential for new business models and significant competitive advantages.
The book is divided in two main parts: First, in a theoretically oriented part, various AI/ML-related approaches like automated Machine Learning, sequence-based learning, Deep Learning, learning from experience and data, and process-aware learning are explained. In a second part, various applications are presented that benefit from the exploitation of recent research results. These include autonomous systems, indoor localization, medical applications, energy supply and networks, logistics networks, traffic control, image processing, and IoT applications.
In the past few years Automated Machine Learning (AutoML) has gained a lot of traction in the Data Science and Machine Learning community. AutoML aims at reducing the partly repetitive work of data scientists and enabling domain experts to construct Machine Learning pipelines without extensive knowledge in Data Science. The Chapter 1 presents a comprehensive review of the current leading AutoML methods and sets AutoML in an industrial context. To this extent we present the typical components of an AutoML system, give an overview over the state-of-the-art and highlight challenges to industrial application by presenting several important topics such as AutoML for time series data, AutoML in unsupervised settings, AutoML with multiple evaluation criteria, or interactive human-in-the-loop methods. Finally, the connection to Neural Architecture Search (NAS) is presented and a brief review with special emphasis on hardware-aware NAS is given.
Reinforcement Learning (RL) is one of the branches of Machine Learning (ML) that aims to learn from the interaction with an environment. In contrast to approaches such as supervised or unsupervised learning, where data samples usually are assigned to a ground truth label (supervised learning) or where they follow some stationary distribution (unsupervised learning), in RL, the agent is learning in direct interaction with the environment. This also defines what data is being collected as a result of which actions are being executed. The agent is hence learning from experience. While more traditionally, RL was focused purely on continuously arriving data, lately also approaches that resort to a given data pool of past environment interactions have gained more and more interest. This chapter covers the basics of RL and discusses the latest research in interactive environments, learning with available data or knowledge, and challenges that arise from the actual deployment of agents to the real world.
Part I Theory
Automated Machine Learning
Sequence-based Learning
Learning from Experience
Learning with Limited Labelled Data
The Role of Uncertainty Quantification for Trustworthy AI
Process-aware Learning
Combinatorial Optimization
Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications
Part II Applications
Assured Resilience in Autonomous Systems – Machine Learning Methods for Reliable Perception
Data-driven Wireless Positioning
Comprehensible AI for Multimodal State Detection
Robust and Adaptive AI for Digital Pathology
Safe and Reliable AI for Autonomous Systems
AI for Stability Optimization in Low Voltage Direct Current Microgrids
Self-Optimization in Adaptive Logistics Networks
Optimization of Underground Train Systems
AI-assisted Condition Monitoring and Failure Analysis for Industrial Wireless Systems
XXL-CT Dataset Segmentation