Become an Agentblazer Champion 2026

Übersicht

Explore core AI concepts and build a basic agent for key tasks.

Lehrplan

• Introduction to AI Concepts
Overview of Artificial Intelligence
History and Evolution of AI
Key AI Terminology and Concepts
• Understanding Intelligent Agents
Definition and Characteristics of Agents
Types of Agents and Their Environments
Rationality and the Performance Measure
• Problem-Solving Techniques
Problem Formulation and Search Strategies
Uninformed Search Algorithms (BFS, DFS)
Informed Search Strategies and Heuristics
Adversarial Search and Game Playing Agents
• Knowledge Representation and Reasoning
Logical Agents and Propositional Logic
First-Order Logic and Inference Techniques
Knowledge-Based Systems and Ontologies
• Machine Learning Basics
Supervised Learning: Regression and Classification
Unsupervised Learning: Clustering and Dimensionality Reduction
Reinforcement Learning Principles
• Developing an AI Agent
Defining Agent Task Environments
Designing the Architecture of an Agent
Implementing Basic Agent Functionality in Code
• Practical Case Studies and Applications
Real-World Applications of AI Agents
Ethical Considerations and Bias in AI
• Hands-On Project: Build Your Agentblazer
Project Requirements and Objectives
Step-by-Step Guide to Developing an Agent
Testing and Evaluation of Your Agent
• Conclusion and Future Directions in AI
Current Trends and Innovations in AI
Future Challenges and Opportunities for AI Agents
Resources for Continued Learning in AI
• Course Wrap-Up and Final Assessment
Review of Key Concepts and Skills Learned
Final Project Presentation and Peer Review
Feedback and Next Steps in AI Learning Journey

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Fachgebiete

Business