| Foundations | Learning & Regression | Learning problem, linear regression, empirical risk, SGD |
| Maximum Likelihood | Entropy, marginal MLE, Gaussian MLE, conditional MLE |
| Classification | Classification intro, perceptron, logistic regression |
| Dimensionality Reduction | PCA, PCA workshop, 3D PCA, low-rank Gaussians |
| Neural Networks | Backpropagation | DNN intro, backprop intro, backprop DNNs, exercises, Fashion MNIST |
| Whitening | Whitening, correlation-covariance matrix |
| Normalization | Batch normalization, layer normalization |
| Regularization | Regularization techniques |
| Hyperparameter Optimization | Bayesian optimization, HPO workshop |
| Transfer Learning | Introduction, tutorial |
| Perception | Sensor Models | Camera models, pinhole model, calibration, beam models, likelihood field |
| CNNs | CNN intro, layers, architectures, small datasets, visualization, ResNet features |
| Scene Understanding | Introduction, detection metrics |
| Faster RCNN Lab | RCNN → Fast RCNN → Faster RCNN, 6-notebook PyTorch series |
| YOLO Lab | YOLO introduction, 5-notebook PyTorch series |
| UNet Lab | UNet architecture, from-scratch notebook |
| Mask RCNN Lab | Mask RCNN, TF demos, PyTorch Detectron2 |
| LLMs | NLP Foundations | NLP pipelines, Word2Vec |
| Recurrent Neural Networks | Introduction, simple RNN, LSTM |
| Language Models | Language models, RNN language model |
| Neural Machine Translation | NMT intro, RNN NMT with attention |
| Transformers | Introduction, single-head attention, multi-head attention, MLP, inference |
| Speech Agents | Text-to-speech and voice cloning |
| Reasoning | Logical Reasoning | Propositional logic, logical inference, logical agents, applications |
| LLM Reasoning | LLM-based reasoning approaches |
| VLMs | Vision-Language Models | Overview, CLIP, LLaVA, BLIP-2 |
| Planning | Task Planning | PDDL, BlocksWorld, logistics, manufacturing |
| Global Planning | Search, forward search, A* |
| Local Planning | Motion planning, behavioral planning, prediction |
| MDPs | Markov Decision Processes | MDP introduction |
| Bellman Equations | Expectation backup, optimality backup, policy improvement, recycling robot |
| Dynamic Programming | Policy iteration |
| RL | Reinforcement Learning | Introduction, model-based algorithms |
| Prediction | Monte Carlo, temporal difference, TD vs MC |
| Control | Generalized policy iteration, greedy MC, SARSA, gridworld |
| Policy-Based | REINFORCE |
| Robotics Systems | Kinematics & Dynamics | Configuration space, homogeneous coordinates, motion representations, wheeled robots |
| State Estimation | Recursive estimation, discrete Bayesian filter, Kalman filters, HMM localization |
| SLAM | Occupancy mapping, simultaneous localization and mapping |
| Systems Integration | Gazebo simulation, ROS applications, Sim2Real, imitation learning |
| Physical AI | VLA Models | Vision-Language-Action agents |
