Course Outline
Introduction
Overview of Artificial Intelligence (AI)
- Machine learning
- Computational intelligence
Understanding the Concepts of Neural Networks
- Generative networks
- Deep neural networks
- Convolution neural networks
Understanding Various Learning Methods
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Semi-supervised learning
Other Computational Intelligence Algorithms
- Fuzzy systems
- Evolutionary algorithms
Exploring Artificial Intelligence Approaches to Optimization
- Choosing AI Approaches Effectively
Learning about Stochastic Dynamic Programming
- Relationship with AI
Implementing Mechatronic Applications with AI
- Medicine
- Rescue
- Defense
- Industry-agnostic trend
Case Study: The Intelligent Robotic Car
Programming the Major Systems of a Robot
- Planning the Project
Implementing AI Capabilities
- Searching and Motion Control
- Localization and Mapping
- Tracking and Controlling
Summary and Next Steps
Requirements
- A foundational understanding of computer science and engineering principles.
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.
