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iphysys // textbook
AI Textbook for Physical Systems.
A structured curriculum covering the mathematics and engineering behind
intelligent physical systems — from foundational theory to advanced topics
in autonomy, edge AI, and distributed intelligence.
Curriculum areas
Mathematical Foundations — Linear algebra, probability, and calculus for machine learning.
Machine Learning — Supervised, unsupervised, and reinforcement learning.
Deep Learning — Neural networks, CNNs, RNNs, transformers, diffusion models.
Computer Vision — Object detection, segmentation, 3D vision, perception stacks.
Physical AI — AI methods applied to robots, sensors, and real-world environments.
Multi-Agent Systems — Distributed intelligence, coordination, and consensus.
Edge AI — Inference optimization and deployment on constrained hardware.
Trustworthy AI — Explainability, calibration, and risk management.
Mission Systems — Human-machine teaming and mission-aware autonomy.