Course Outline
Introduction
Overview of Artificial Intelligence (AI)
- Machine learning
- Computational intelligence
Exploring Neural Network Concepts
- Generative networks
- Deep neural networks
- Convolutional neural networks
Examining 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
- Selecting AI Strategies Effectively
Understanding Stochastic Dynamic Programming
- Its relationship with AI
Applying AI to Mechatronic Systems
- Medicine
- Rescue operations
- Defense
- Cross-industry trends
Case Study: The Intelligent Robotic Car
Programming Key Systems in Robotics
- Project Planning
Implementing AI Features
- Search algorithms and Motion Control
- Localization and Mapping
- Tracking and Control mechanisms
Summary and Recommended Next Steps
Requirements
- Foundational knowledge in computer science and engineering
Target Audience
- Engineers
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.