Learning, Reasoning, and Planning with Neuro-Symbolic Concepts

Overview

Explore neuro-symbolic concepts for AI learning, reasoning, and planning with Jiayuan Mao, who presents a framework combining symbolic programs and neural networks for improved data efficiency and generalization.

Syllabus

• **Introduction to Neuro-Symbolic AI**
Overview of Neuro-Symbolic Concepts
Historical Context and Motivation
• **Symbolic AI Review**
Logic-Based Reasoning
Symbolic Planning Methods
• **Neural Networks Fundamentals**
Overview of Neural Network Architectures
Deep Learning Basics
• **Integration of Symbolic and Neural Approaches**
Framework for Combining Symbolic and Neural Methods
Case Studies of Neuro-Symbolic Systems
• **Learning with Neuro-Symbolic Models**
Data Efficiency in Neuro-Symbolic Learning
Representation Learning in Hybrid Systems
• **Reasoning with Neuro-Symbolic Methods**
Symbolic Reasoning in Neural Networks
Probabilistic and Logical Reasoning Integration
• **Planning with Neuro-Symbolic Techniques**
Planning Algorithms in Neuro-Symbolic Contexts
Applications to Real-World Problems
• **Case Studies and Applications**
Examples from Industry and Research
Best Practices in Implementing Neuro-Symbolic Systems
• **Future Directions in Neuro-Symbolic AI**
Emerging Trends and Technologies
Open Challenges and Research Opportunities
• **Hands-on Projects and Assignments**
Implementation of a Basic Neuro-Symbolic System
Experimentation with Real-World Datasets
• **Review and Wrap-up**
Summary of Key Learnings
Discussion of Future Applications and Career Paths in Neuro-Symbolic AI

Subjects

Computer Science