What You’ll Learn
This book bridges multiple disciplines to provide a unified understanding of AI agents:Foundations
Statistical learning theory, regression, classification, and optimization fundamentals.
Deep Neural Networks
Backpropagation, normalization, regularization, and training techniques.
Perception
CNNs, sensor models, and visual processing for understanding the environment.
Kinematics
Configuration spaces, motion representations, and robot geometry.
Planning
Task planning, global planning, and local planning for autonomous navigation.
Reinforcement Learning
MDPs, value-based methods, and policy-based algorithms.
Prerequisites
This book assumes familiarity with:- Linear algebra and calculus
- Probability and statistics
- Python programming
- Basic machine learning concepts