Unlocking Artificial Intelligence : : From Theory to Applications.

• Author: Mutschler, Christopher.
• TeilnehmendeR: Münzenmayer, Christian.; Uhlmann, Norman.; Martin, Alexander.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2024.; ©2024.
• Year of Publication: 2024
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (382 pages)

Table of Contents

Intro -- 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 &amp -- 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.