Part I: Core Problems -- Chapter 1: Neural Network Models for Time Series Data -- Chapter 2: Self-Supervised Learning from Unlabeled IoT Data -- Chapter 3: On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models -- Chapter 4: Out of Distribution Detection -- Chapter 5: Model Compression for Edge Computing -- Part II: Distributed Problems -- Chapter 6: Communication Efficient Distributed Learning -- Chapter 7: Coreset-based Data Reduction for Machine Learning at the Edge -- Chapter 8: Lightweight Collaborative Perception at the Edge -- Chapter 9: Dynamic Placement of Services at the Edge -- Chapter 10: Joint Service Placement and Request Scheduling at the Edge -- Part III: Cross-cutting Thoughts -- Chapter 11: Criticality-based Data Segmentation and Resource Allocation in Machine Inference Pipelines -- Chapter 12: Model Operationalization at Edge Devices.