Course Outline
Introduction
Overview of Artificial Intelligence (AI)
- Machine learning
- Computational intelligence
Conceptual Understanding of Neural Networks
- Generative networks
- Deep neural networks
- Convolution neural networks
Exploring Various Learning Methodologies
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Semi-supervised learning
Additional Computational Intelligence Algorithms
- Fuzzy systems
- Evolutionary algorithms
Examining AI Approaches to Optimization
- Effective Selection of AI Strategies
Insights into Stochastic Dynamic Programming
- Connection to AI
Applying AI to Mechatronic Systems
- Medicine
- Rescue operations
- Defense sector
- Cross-industry trends
Case Study: The Intelligent Robotic Car
Programming Key Systems of a Robot
- Project Planning
Implementing AI Functionalities
- Search algorithms and motion control
- Localization and mapping
- Tracking and control mechanisms
Summary and Future Directions
Requirements
- Foundational knowledge of computer science and engineering.
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.