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.