| 1 | Introduction | AI and robotics systems perspective with autonomous vehicles focus. Prerequisites review. |
| 2 | Perception with DNNs | Multi-modal sensing, maximum likelihood optimization, fully connected and convolutional architectures. |
| 3 | Object Detection | MaskRCNN, YOLO, transfer learning with pretrained feature extractors. |
| 4 | Segmentation | Semantic and instance segmentation for complex scene planning. |
| 5 | State Estimation | Recursive estimation, Dynamic Bayesian Networks, Kalman filters. Reading: THRUN Chapters 2-3 |
| 6 | Localization | Egomotion, velocity/odometry models, pose estimation. Reading: THRUN Chapters 5, 7; CORKE Chapter 4 |
| 7 | SLAM | Simultaneous Localization and Mapping, Visual SLAM with monocular cameras. Reading: THRUN Chapters 9-10; CORKE Chapter 6 |
| 8 | Global Planning | Optimal planning under uncertainty, A*, D*, RRT*, PRM algorithms. |
| 9 | MDPs and POMDPs | Sequential decisions, reward signals, Bellman equations. |
| 10 | Deep RL | Model-free methods for robotic control policies. |
| 11 | Instruction Following | Vision Language Models for human-robot collaboration. |
| 12 | VLA Models | End-to-end Vision-Language-Action models for instruction parsing, perception, and action. |
| 13 | Review | Final exam preparation |
