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Hybrid and Neurosymbolic AI
Integrate Symbolic Reasoning with Deep Learning to Build Explainable, Smarter AI Systems
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Hybrid and Neurosymbolic AI combines the best of both worlds — the pattern recognition capabilities of neural networks and the logical reasoning strengths of symbolic AI. This Uplatz course provides a deep, structured understanding of how to integrate connectionist learning (deep neural networks) with symbolic reasoning (logic, rules, and knowledge graphs) to create transparent, explainable, and robust AI systems.
What is it?
Traditional AI has two dominant paradigms:
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Symbolic AI, which represents knowledge explicitly using symbols, rules, and logic (e.g., expert systems).
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Neural AI, which learns patterns from data using deep learning.
Neurosymbolic AI merges these paradigms, enabling machines not only to learn from data but also to reason abstractly and generalize across contexts. This hybrid approach is crucial for developing explainable AI (XAI), semantic reasoning, and trustworthy AI systems used in domains like robotics, law, healthcare, and scientific discovery.
How to use this course
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Start with the conceptual foundations of symbolic reasoning and deep learning.
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Understand knowledge representation — ontologies, logic programming, and inference.
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Explore neural-symbolic integration frameworks like DeepProbLog, TensorLog, and Logical Neural Networks (LNNs).
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Build hybrid models that combine perception (neural) and reasoning (symbolic) modules.
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Apply use cases in visual reasoning, language understanding, and decision support.
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Complete the capstone project by designing a neurosymbolic reasoning pipeline using Python and logic-based modules.
By the end, learners will understand how to engineer AI systems that see, think, and explain — bringing reasoning and learning together for the next generation of intelligent systems.
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Understand the differences and connections between symbolic and neural AI.
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Learn the fundamentals of knowledge representation and logical inference.
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Explore frameworks for neurosymbolic integration.
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Implement hybrid AI systems combining learning and reasoning.
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Apply neurosymbolic principles in NLP, vision, and robotics.
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Improve model interpretability and explainability.
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Develop reasoning over structured and unstructured data.
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Integrate graph-based and probabilistic logic into deep learning pipelines.
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Evaluate neurosymbolic models for accuracy and transparency.
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Prepare for advanced roles in AI research and cognitive computing.
Course Syllabus
Module 1: Introduction to Symbolic and Neural AI
Module 2: Knowledge Representation and Logical Reasoning
Module 3: Deep Learning Foundations for Hybrid Integration
Module 4: Neurosymbolic Architectures and Frameworks
Module 5: Logical Neural Networks and TensorLog
Module 6: Integrating Reasoning with Perception Models
Module 7: Explainability and Trust in Hybrid AI
Module 8: Applications – NLP, Vision, and Robotics
Module 9: Evaluation, Transparency, and Ethical Considerations
Module 10: Capstone Project – Build a Hybrid AI System
Upon completion, learners receive a Certificate of Completion from Uplatz, validating their expertise in Hybrid and Neurosymbolic AI. This Uplatz certification highlights your ability to design AI systems that combine data-driven learning with logical reasoning and knowledge-based inference.
The certification aligns with the latest developments in explainable AI, cognitive computing, and hybrid model design, equipping learners with the technical depth needed to create interpretable, safe, and human-aligned AI systems. It is ideal for professionals in AI engineering, research, and data science who seek to move beyond traditional deep learning and into the realm of reasoning-driven intelligence.
Holding this certification establishes you as a professional ready to contribute to the future of transparent, trustworthy, and hybrid intelligent systems.
As AI advances toward explainability and reasoning, Neurosymbolic AI is becoming one of the most promising and in-demand specializations. Completing this course from Uplatz prepares learners for roles such as:
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Neurosymbolic AI Engineer
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AI Research Scientist (Hybrid Systems)
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Explainable AI Specialist
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Knowledge Graph Engineer
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Cognitive Computing Developer
Professionals in this field typically earn between $110,000 and $190,000 per year, depending on experience and sector.
Career opportunities exist in AI research labs, robotics companies, healthcare analytics, autonomous systems, and government R&D agencies seeking trustworthy, interpretable AI systems.
This course bridges the gap between human reasoning and deep learning, preparing learners to create next-generation AI systems that are both intelligent and explainable.
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What is Neurosymbolic AI?
It’s the integration of symbolic reasoning and neural network learning into a single, unified AI system. -
Why do we need Hybrid AI systems?
Because they combine the interpretability of symbolic AI with the flexibility and pattern recognition of neural AI. -
What are examples of symbolic AI techniques?
Logic programming, ontologies, semantic networks, and expert systems. -
How do neural networks differ from symbolic systems?
Neural networks learn patterns from data; symbolic systems reason using explicit rules. -
What is a Logical Neural Network (LNN)?
A hybrid model that embeds logic rules directly into neural network architectures. -
What are common frameworks for neurosymbolic AI?
DeepProbLog, TensorLog, and Logical Tensor Networks. -
What are the benefits of Hybrid AI?
Better reasoning, improved explainability, and reduced data dependency. -
What are the main challenges of Neurosymbolic AI?
Model complexity, integration of logic and learning, and computational cost. -
How can neurosymbolic AI improve explainability?
By using symbolic reasoning to justify neural network predictions. -
Where is Hybrid AI applied today?
In robotics, law tech, healthcare diagnostics, natural language understanding, and autonomous systems.