AI+ Robotics (AT-420 Self-Paced Training)
Ziele der Schulung
The AI+ Robotics certification is designed to equip learners with a comprehensive understanding of how Artificial Intelligence (AI) integrates with robotics systems, covering foundational robotics principles, machine learning and deep learning in robotics, autonomous systems, development frameworks, and emerging technologies. It prepares candidates to apply AI techniques to design, optimize, and deploy intelligent robotic solutions and enhances professional credibility in AI-driven automation and robotics roles.
Zielgruppe Seminar
- Robotics Engineers aiming to enhance robotic system design and functionality with AI
- Mechanical Engineers seeking to integrate AI to optimize robotics performance
- AI Specialists applying AI techniques to improve robot autonomy and intelligence
- IT Specialists and System Integrators implementing AI-powered robotics solutions
- Students and New Graduates preparing for careers in AI and robotics
Voraussetzungen
- Familiarity with basic concepts of Artificial Intelligence (AI)
- Openness to generate innovative ideas and leverage AI tools
- Critical analysis skills to evaluate AI and robotics technologies
- Readiness to engage in problem-solving and apply AI techniques in real scenarios
Seminarinhalt
Module 1: Introduction to Robotics and Artificial Intelligence (AI)
1.1 Overview of Robotics: Introduction, History, Evolution, and Impact 1.2 Introduction to Artificial Intelligence (AI) in Robotics 1.3 Fundamentals of Machine Learning (ML) and Deep Learning 1.4 Role of Neural Networks in Robotics
Module 2: Understanding AI and Robotics Mechanics
2.1 Components of AI Systems and Robotics 2.2 Deep Dive into Sensors, Actuators, and Control Systems 2.3 Exploring Machine Learning Algorithms in Robotics
Module 3: Autonomous Systems and Intelligent Agents
3.1 Introduction to Autonomous Systems 3.2 Building Blocks of Intelligent Agents 3.3 Case Studies: Autonomous Vehicles and Industrial Robots 3.4 Key Platforms for Development: ROS (Robot Operating System)
Module 4: AI and Robotics Development Frameworks
4.1 Python for Robotics and Machine Learning 4.2 TensorFlow and PyTorch for AI in Robotics 4.3 Introduction to Other Essential Frameworks
Module 5: Deep Learning Algorithms in Robotics
5.1 Understanding Deep Learning: Neural Networks, CNNs 5.2 Robotic Vision Systems: Object Detection and Recognition 5.3 Hands-on Session: Training a CNN for Object Recognition 5.4 Use Case: Precision Manufacturing with Robotic Vision
Module 6: Reinforcement Learning in Robotics
6.1 Basics of Reinforcement Learning (RL) 6.2 Implementing RL Algorithms for Robotics 6.3 Hands-on Session: Developing RL Models for Robots 6.4 Use Case: Optimizing Warehouse Operations with RL
Module 7: Generative AI for Robotic Creativity
Module 8: Natural Language Processing (NLP) for Human-Robot Interaction
Module 9: Practical Activities and Use-Cases
Module 10: Emerging Technologies and Innovation in Robotics
Module 11: Exploring AI with Robotic Process Automation
Module 12: AI Ethics, Safety, and Policy
Module 13: Innovations and Future Trends in AI and Robotics
Hinweise
Prüfung und Zertifizierung
- Exam Details
- Duration: 90 minutes
- Passing Score: 70%
- Format: 50 multiple-choice and multiple-response questions
- Delivery Method: Online, proctored examination
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