9 modules · 43 lessons · 10+ hours of content
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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.
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
How We Understand Scenes
Human Perception and Imaging.
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. Global Planning
7. Global 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. Multimodal Reasoning and Transformers
8. Multimodal Reasoning and Transformers
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.