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.