| 1 | Introduction | AI and robotics systems perspective with autonomous vehicles focus. Prerequisites review. |
| 2 | Statistical Learning Theory | The learning problem, linear regression, gradient descent, maximum likelihood and classification. |
| 3 | Dense and Convolutional Neural Networks | Multi-modal sensing, maximum likelihood optimization, fully connected and convolutional architectures. |
| 4 | Object Detection | MaskRCNN, YOLO, transfer learning with pretrained feature extractors. |
| 5 | State Estimation | Recursive estimation, Dynamic Bayesian Networks, Kalman filters. Reading: THRUN Chapters 2-3 |
| 6 | Midterm Exam | No lecture. A take-home, programming-oriented midterm covering Weeks 1-5. |
| 7 | SLAM | Simultaneous Localization and Mapping, including localization and egomotion, and Visual SLAM with monocular cameras. Reading: THRUN Chapters 5, 7, 9-10; CORKE Chapters 4, 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 Reinforcement Learning | 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 | Sim-to-Real Transfer | Closing the gap between simulated training and physical deployment. |
