Course Outline
Introduction
Overview of Artificial Intelligence (AI)
- Machine learning
- Computational intelligence
Conceptual Understanding of Neural Networks
- Generative networks
- Deep neural networks
- Convolutional neural networks
Exploring Various Learning Methods
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Semi-supervised learning
Additional Computational Intelligence Algorithms
- Fuzzy systems
- Evolutionary algorithms
Investigating AI Approaches to Optimization
- Effectively Choosing AI Approaches
Insights into Stochastic Dynamic Programming
- Connection to AI
Applying Mechatronic Applications with AI
- Medical applications
- Rescue operations
- Defense sectors
- Industry-agnostic trends
Case Study: The Intelligent Robotic Car
Programming the Core Systems of a Robot
- Project Planning
Implementing AI Capabilities
- Search algorithms and motion control
- Localization and mapping
- Tracking and control mechanisms
Summary and Recommended Next Steps
Requirements
- A foundational grasp of computer science and engineering principles
Target Audience
- Engineering professionals
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.