1. Course Introduction
1. Course Introduction
The Robotic AI Agent - A practical map for navigating robotic AI systems.
Development Environment Setup - Setting up the ROS2 Docker-based development environment.
Mathematical Prerequisites - What you need to know before diving into the course material.
How We Understand Scenes - Human Perception and Imaging.
2. Statistical Learning Theory
2. Statistical Learning Theory
The Learning Problem - The Vapnik block diagram.
Linear Regression - Extracting non-linear patterns with linear models.
Gradient Descent - Optimizing complicated functions with iterative methods.
Entropy - Information theory principles.
Maximum Likelihood Estimation - The workhorse of statistical modeling.
Binary Classification - Binary classification and Logistic Regression.
3. Neural Networks
3. Neural Networks
Feature Extraction - Using a simple network to understand how features are extracted.
Multiclass Classifier - A simple multiclass classifier example.
Backpropagation - How to calculate gradients in a neural network.
Regularization - How to regulate the complexity of a neural network.
4. Convolutional Neural Networks
4. Convolutional Neural Networks
Convolution and Correlation - A linear operation for extracting spatial features.
CNN Architectures - Looking inside a CNN layer and understanding architectural patterns.
Image Classification - Image classification with data augmentation.
What CNNs Learn - Visualizing the features learned by CNNs.
ResNets - Residual Networks and skip connections.
5. Object Detection
5. Object Detection
Introduction to Object Detection - Object detection in a physical security application.
Computer Vision Datasets - What types of annotations are used in computer vision?
Region-based Object Detectors - R-CNN, Fast R-CNN, Faster R-CNN.
6. Recursive State Estimation
6. Recursive State Estimation
Introduction to State Estimation with HMM - Introducing Hidden Markov Models.
The Bayes Filter - Implementing the Bayes filter algorithm.
Discrete Bayes Filter Example - Discrete Bayes localization notebook.
Continuous State Space and Kalman Filter - Localizing a drone under Gaussian assumptions.
A Kalman Filter Example - Kalman localization notebook.
7. Task Planning
7. Task Planning
Introduction to Planning - Typical planning problems and PDDL.
Planning Domain Definition Language - The constructs of PDDL.
Forward Search Algorithms - Finding global planning solutions with forward search.
The A Algorithm* - Using heuristics to guide forward search.
8. Large Language Models
8. Large Language Models
Introduction to Transformers - The transformer architecture and the simple attention mechanism.
The Learnable Attention Mechanism - Implementing the scaled dot-product self attention mechanism.
Multi-Head Self Attention - Using multiple attention heads to capture different aspects of input sequences.
9. Markov Decision Processes
9. Markov Decision Processes
Introduction to MDPs - Part 1 - Defining Markov Decision Processes.
Introduction to MDPs - Part 2 - Defining Markov Decision Processes.
Bellman Expectation Equations - Part 1 - Deriving the Bellman Expectation Equations.
Bellman Expectation Equations - Part 2 - Deriving the Bellman Expectation Equations.
Policy Evaluation - Part 1 - Using the Bellman Expectation Equations for Policy Evaluation.
Policy Evaluation - Part 2 - Using the Bellman Expectation Equations for Policy Evaluation.
Bellman Optimal Value Functions - Deriving the Bellman Optimality Equations.
Policy Iteration and Value Iteration - Using the Bellman Optimality Equations for optimal control.