Preface
Acknowledgements
Contents
Part I Theory
	Chapter 1 Automated Machine Learning
		1.1 Introduction
		1.2 Components of AutoML Systems
			1.2.1 Search Space
			1.2.2 Optimization
			1.2.3 Ensembling
			1.2.4 Feature Selection and Engineering
			1.2.5 Meta-Learning
			1.2.6 A Brief Note on AutoML in the Wild
		1.3 Selected Topics in AutoML
			1.3.1 AutoML for Time Series Data
			1.3.2 Unsupervised AutoML
			1.3.3 AutoML Beyond a Single Objective
			1.3.4 Human-In-The-Loop AutoML
		1.4 Neural Architecture Search
			1.4.1 A Brief Overview of the Current State of NAS
			1.4.2 Hardware-aware NAS
		1.5 Conclusion and Outlook
		References
	Chapter 2 Sequence-based Learning
		2.1 Introduction
		2.2 Time Series Processing
			2.2.1 Time Series Data Streams
			2.2.2 Pre-Processing
			2.2.3 Predictive Modelling
			2.2.4 Post-Processing
		2.3 Methods
			2.3.1 Temporal Convolutional Networks
			2.3.2 Recurrent Neural Networks
			2.3.3 Transformer
		2.4 Perspectives
			2.4.1 Time Series Similarity
				2.4.1.1 Deep Metric Learning
			2.4.2 Transfer Learning & Domain Adaptation
			2.4.3 Model Interpretability
				2.4.3.1 Interpretability for Time Series
				2.4.3.2 Trusting Interpretations
		2.5 Conclusion and Outlook
		Acknowledgments
		References
	Chapter 3 Learning from Experience
		3.1 Introduction
		3.2 Concepts of Reinforcement Learning
			3.2.1 Markov Decision Processes (MDPs)
			3.2.2 Dynamic Programming
			3.2.3 Model-free Reinforcement Learning
			3.2.4 General Remarks
		3.3 Learning purely through Interaction
			3.3.1 Exploration-Exploitation
				3.3.1.1 Exploration Strategies
				3.3.1.2 Exploration in Deep RL
		3.4 Learning with Data or Knowledge
			3.4.1 Model-based RL with continuous Actions
			3.4.2 MBRL with Discrete Actions: Monte Carlo Tree Search
			3.4.3 Offline Reinforcement Learning
			3.4.4 Hierarchical RL
		3.5 Challenges for Agent Deployment
			3.5.1 Safety through Policy Constraints
			3.5.2 Generalizability of Policies
			3.5.3 Lack of a Reward Function
		3.6 Conclusion and Outlook
		References
	Chapter 4 Learning with Limited Labelled Data
		4.1 Introduction
		4.2 Semi-Supervised Learning
			4.2.1 Classical Semi-Supervised Learning
			4.2.2 Deep Semi-Supervised Learning
				4.2.2.1 Self-training
				4.2.2.2 Unsupervised Regularization
			4.2.3 Self-Training and Consistency Regularization
		4.3 Active Learning
			4.3.1 Deep Active Learning (DAL)
			4.3.2 Uncertainty Sampling
			4.3.3 Diversity Sampling
			4.3.4 Balanced Criteria
		4.4 Active Semi-Supervised Learning
			4.4.1 How can SSL and ALWork Together?
			4.4.2 Are SSL and AL Always Mutually Beneficial?
		4.5 Conclusion and Outlook
		References
	Chapter 5 The Role of Uncertainty Quantification for Trustworthy AI
		5.1 Introduction
		5.2 Towards Trustworthy AI
			5.2.1 The EU AI Act
			5.2.2 From Uncertainty to Trustworthy AI
		5.3 Uncertainty Quantification
			5.3.1 Sources of Uncertainty
				5.3.1.1 Aleatoric Uncertainty
				5.3.1.2 Epistemic Uncertainty
			5.3.2 Methods for Quantification of Uncertainty and Calibration
				5.3.2.1 Data-based Methods
				5.3.2.2 Architecture-Modifying Methods
				5.3.2.3 Post-Hoc Methods
			5.3.3 Evaluation Metrics for Uncertainty Estimation
				5.3.3.1 Negative Log-Likelihood
				x
				5.3.3.2 Expected Calibration Error
				5.3.3.3 Rejection-based Measures
		5.4 Conclusion and Outlook
		References
	Chapter 6 Process-aware Learning
		6.1 Introduction
		6.2 Overview of Process Mining
			6.2.1 Process Mining Basic Concept
			6.2.2 Process Mining Types
				6.2.2.1 Process Discovery
				6.2.2.2 Conformance Checking
				6.2.2.3 Model Enhancement
			6.2.3 Event Log
			6.2.4 Four Quality Criteria
			6.2.5 Types of Processes
				6.2.5.1 Lasagna Processes
				6.2.5.2 Spaghetti Processes
		6.3 Process-Awareness from Theory to Practice
			6.3.1 Predictive Analysis in Process Mining
			6.3.2 Predictive Process Mining with Bayesian Statistics
				6.3.2.1 Preliminaries for Bayesian Modeling
				6.3.2.2 Quality Criteria for Bayesian Modeling
				6.3.2.3 Context-Aware Structure Learning for Probabilistic Process Prediction
			6.3.3 Process AI
		6.4 Conclusion and Outlook
		References
	Chapter 7 Combinatorial Optimization
		7.1 Introduction
		7.2 Solving Methods
			7.2.1 Heuristics
			7.2.2 Exact Methods
		7.3 Modeling Techniques
			7.3.1 Graph Theory
				7.3.1.1 Clique Problems
				7.3.1.2 Flow Models
			7.3.2 Mixed Integer Programs and Connections to Machine Learning
				7.3.2.1 Modeling Logic
				7.3.2.2 Binary Decision Trees
			7.3.3 Pooling
		7.4 Conclusion and Outlook
		References
	Chapter 8 Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications
		8.1 Introduction
		8.2 Approaches to Acquire Semantics
			8.2.1 Manual Annotation and Labeling
			8.2.2 Data Augmentation Techniques
			8.2.3 Simulation and Generation
				8.2.3.1 Physical Modeling
				8.2.3.2 Generative Adversarial Networks
			8.2.4 High-End Reference Sensors
			8.2.5 Active Learning
			8.2.6 Knowledge Modeling Using Semantic Networks
			8.2.7 Discussion
		8.3 Conclusion and Outlook
		References
Part II Applications
	Chapter 9 Assured Resilience in Autonomous Systems – Machine Learning Methods for Reliable Perception
		9.1 Introduction
			9.1.1 The Perception Challenge
		9.2 Approaches to reliable perception
			9.2.1 Choice of Dataset
			9.2.2 Unexpected Behavior of ML Methods
			9.2.3 Reliable Object Detection for Autonomous Driving
			9.2.4 Uncertainty Quantification for Image Classification
			9.2.5 Ensemble Distribution Distillation for 2D Object Detection
			9.2.6 Robust Object Detection in Simulated Driving Environments
				9.2.6.1 Scenarios Setup
				9.2.6.2 Methods and Metrics
				9.2.6.3 Results
			9.2.7 Out-of-Distribution Detection
		9.3 Conclusion and Outlook
		References
	Chapter 10 Data-driven Wireless Positioning
		10.1 Introduction
		10.2 AI-Assisted Localization
		10.3 Direct Positioning
			10.3.1 Model
			10.3.2 Experimental Setup
				10.3.2.1 Measurement Campaign
				10.3.2.2 Environments
			10.3.3 Evaluation
			10.3.4 Hybrid Localization
			10.3.5 Zone Identification
			10.3.6 Experimental Setup
			10.3.7 Environments
			10.3.8 Evaluation
		10.4 Conclusion and Outlook
		Acknowledgements
		References
	Chapter 11 Comprehensible AI for Multimodal State Detection
		11.1 Introduction
			11.1.1 Cognitive Load Estimation
			11.1.2 Challenges in Affective Computing
		11.2 Data Collection
			11.2.1 Annotation
			11.2.2 Data Preprocessing
		11.3 Modeling
			11.3.1 In-Domain Evaluation
			11.3.2 Cross-Domain Evaluation
			11.3.3 Interpretability
			11.3.4 Improving ECG Representation Learning
			11.3.5 Deployment and Application
		11.4 Conclusion and Outlook
		References
	Chapter 12 Robust and Adaptive AI for Digital Pathology
		12.1 Introduction
		12.2 Applications: Tumor Detection and Tumor-Stroma Assessment
			12.2.1 Generation of Labeled Data Sets
			12.2.2 Data Sets for Tumor Detection
				12.2.2.1 Primary Data Set
				12.2.2.2 Multi-Scanner Dataset
				12.2.2.3 Multi-Center Dataset
				12.2.2.4 Out-of-Distribution Data Set
				12.2.2.5 Urothelial Data Sets
			12.2.3 Data Set for Tumor-Stroma Assessment
		12.3 Prototypical Few-Shot Classification
			12.3.1 Robustness through Data Augmentation
				12.3.1.1 Evaluation on the Multi-Scanner Data Set
				12.3.1.2 Evaluation on the Multi-Center Data Set
			12.3.2 Out-of-Distribution Detection
			12.3.3 Adaptation to Urothelial Tumor Detection
			12.3.4 Interactive AI Authoring with MIKAIA®
		12.4 Prototypical Few-Shot Segmentation
			12.4.1 Tumor-Stroma Assessment
		12.5 Conclusion and Outlook
		Acknowledgements
		References
	Chapter 13 Safe and Reliable AI for Autonomous Systems
		13.1 Introduction
			13.1.1 Reinforcement Learning
			13.1.2 Reinforcement Learning for Autonomous Driving
		13.2 Generating Environments with Driver Dojo
			13.2.1 Method
		13.3 Training safe Policies with SafeDQN
			13.3.1 Method
			13.3.2 Evaluation
		13.4 Extracting tree policies with SafeVIPER
			13.4.1 Training the Policy
			13.4.2 Verification of Decision Trees
			13.4.3 Evaluation
		13.5 Conclusion and Outlook
		References
	Chapter 14 AI for Stability Optimization in Low Voltage Direct Current Microgrids
		14.1 Introduction
		14.2 Low Voltage DC Microgrids
			14.2.1 Control of Low Voltage DC Microgrids
			14.2.2 Stability of Low Voltage DC Microgrids
		14.3 AI-based Stability Optimization for Low Voltage DC Microgrids
			14.3.1 Overview
			14.3.2 Digital Network Twin and Generation of Labels to Describe the Stability State
			14.3.3 LVDC Microgrid Surrogate Model Applying Random Forests
			14.3.4 Stability Optimization Applying Decision Trees
		14.4 Implementation and Assessment
			14.4.1 Measurement of Grid Stability
			14.4.2 Experimental Validation
		14.5 Conclusion and Outlook
		References
	Chapter 15 Self-Optimization in Adaptive Logistics Networks
		15.1 Introduction
		15.2 A Brief Overview of Relevant Literature on Predicting the All-Time Buy Quantity
		15.3 Predicting the All-Time Buy
		15.4 A Probabilistic Hierarchical Growth Curve model
		15.5 Determining the Optimal Order Policy
			15.5.1 Modeling Non-Linear Costs
			15.5.2 Robust Optimization
		15.6 Pooling
		15.7 Conclusion and Outlook
		References
	Chapter 16 Optimization of Underground Train Systems
		16.1 Optimization of DC Railway Power Systems
			16.1.1 Introduction
			16.1.2 Optimal Power Flow and mathematical MIQCQP model
				16.1.2.1 Snapshot Model
				16.1.2.2 Time Span Model
			16.1.3 Case Studies
				16.1.3.1 Optimization of time stamps in a small network
				16.1.3.2 Optimization of a realistic entire line
		16.2 Energy-Efficient Timetabling applied to a German Underground System
			16.2.1 Industrial Challenge and Motivation
			16.2.2 Mathematical Research
			16.2.3 Implementation
		16.3 Conclusion and Outlook
		Acknowledgements
		References
	Chapter 17 AI-assisted Condition Monitoring and Failure Analysis for IndustrialWireless Systems
		17.1 Introduction
		17.2 Verifying Data Source Accuracy in Protocol Analysis
			17.2.1 System Concept
			17.2.2 Autoencoder Architecture for Anomaly Detection
			17.2.3 Dataset and Performance Evaluation
		17.3 Automated and User-friendly Spectral Analysis
			17.3.1 ML-based Spectrum Analysis
			17.3.2 Generation of Training and Validation Data
			17.3.3 Model Validation Using Artificial and Measurement Data
			17.3.4 System Architecture
		17.4 Cross-layer Analysis
			17.4.1 Variable Adaptive Dynamic Time Warping: A Novel Approach
			17.4.2 Experimental Results and Discussion
			17.4.3 Implications for Research and Beyond
		17.5 Conclusion and Outlook
		References
	Chapter 18 XXL-CT Dataset Segmentation
		18.1 Introduction
		18.2 XXL-CT Dataset Acquisition
			18.2.0.1 Me163 Airplane
			18.2.0.2 Honda Accord Vehicle
		18.3 Annotation Pipelines
			18.3.1 3D Instance Labelling Pipeline
			18.3.2 3D Semantic Labelling Pipeline
		18.4 Training Infrastructure and Segmentation Results
			18.4.1 Instance Segmentation
			18.4.2 Semantic Segmentation
		18.5 Conclusion and Outlook
		Acknowledgments
		References
	Chapter 19 Energy-Efficient AI on the Edge
		19.1 AI on the Edge
		19.2 Energy-Efficient Classical Machine Learning
			19.2.1 Classification of Time Series Data
			19.2.2 Multi-Objective Optimization
			19.2.3 Energy Prediction for Classical Machine Learning
			19.2.4 EA-AutoML Tool
			19.2.5 Application Example
		19.3 Energy-Efficient Deep Learning
			19.3.1 Deep Compression
				19.3.1.1 Pruning
				19.3.1.2 Quantization
			19.3.2 Efficient Design Space Exploration
			19.3.3 Benchmarking Edge AI
		19.4 Conclusion and Outlook
		References