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Starts 6 June 2025 12:15
Ends 6 June 2025
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Overview
Explore advanced techniques for reasoning with large-scale environment models in AI systems, focusing on theoretical aspects of trustworthy artificial intelligence.
Syllabus
- Introduction to Large-Scale Environment Models
- Theoretical Foundations of Environment Modeling
- Scalability in Reasoning
- Trustworthy AI: Ensuring Reliability and Safety
- Advanced Reasoning Techniques
- Knowledge Representation and Ontologies
- Handling Uncertainty in Environment Models
- Experimentation and Evaluation of AI Models
- Emerging Trends and Future Directions
- Course Conclusion
Overview of environment models in AI
Importance and challenges of large-scale models
Probabilistic graphical models
Bayesian networks and reasoning
Markov decision processes
Parallelization techniques
Efficient data structures for large environments
Distributed computing paradigms
Definitions and metrics of trustworthiness
Formal verification methods
Robustness to adversarial attacks
Approximate inference methods
Monte Carlo methods and sampling strategies
Deep reinforcement learning integration
Semantic models for environment representation
Ontology integration for enhanced reasoning
Techniques for managing uncertainty
Risk assessment and mitigation strategies
Evaluation metrics for reasoning systems
Case studies and real-world applications
Current research and innovation areas
Future challenges in large-scale reasoning
Recap of key concepts
Discussion on future ethical considerations in AI reasoning systems
Subjects
Computer Science