Probabilistic Foundations of Metacognition via Hybrid AI

Overview

Engage with an innovative approach to understanding metacognition through a probabilistic lens by leveraging hybrid artificial intelligence techniques. This online course delivers insights into how symbolic methods and deep learning can be integrated to interpret and explain significant experimental results within the domain of metacognition.

Ideal for those interested in artificial intelligence and computer science, this comprehensive course materials are available through YouTube. Expand your knowledge on the latest advancements in AI and explore the complex interplay between different paradigms and methodologies involved in the field.

Begin your journey into the future of cognitive science with this unique educational opportunity provided by the University, hosted seamlessly online for your convenience.

Syllabus

• Introduction to Metacognition
Definition and importance of metacognition in cognitive science
Overview of traditional and contemporary theories
• Fundamentals of Probabilistic Reasoning
Probability theory basics
Bayesian inference and its applications
Probabilistic graphical models
• Overview of Hybrid AI
Defining hybrid AI: Combining symbolic methods and deep learning
Advantages and challenges of hybrid approaches
• Symbolic Methods in AI
Logic-based systems and rule-based reasoning
Applications of symbolic AI in cognitive modeling
• Deep Learning Foundations
Neural network architectures
Techniques for training deep learning models
Interpretability in deep learning
• Probabilistic Models in Deep Learning
Introduction to probabilistic neural networks
Variational inference and probabilistic programming
• Hybrid AI for Metacognition
Integrating symbolic reasoning with deep learning
Case studies of hybrid AI systems in cognitive tasks
• Probabilistic Approach to Metacognition
Developing a probabilistic framework for metacognition
Modeling human-like reflective thinking in AI
• Experimental Results and Analysis
Overview of experimental studies in metacognition
Analyzing hybrid AI models against empirical data
• Applications and Future Directions
Implementing metacognitive features in AI systems
Ethical and practical implications of metacognitive AI
• Course Conclusion and Project
Developing a hybrid AI model with metacognitive capabilities
Summary of key concepts and future research directions

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Subjects

Computer Science