Learning Path:

1. Introduction to Expert Systems:

   • Resources:
     • Expert Systems - GeeksforGeeks
     • [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 20)]
   • Notes: Define expert systems, components, and examples.

2. Inference (Forward and Backward Chaining):

   • Resources:
     • Inference in Expert Systems - Medium
   • Notes: Explain forward and backward chaining, examples, and applications.

3. Languages and Tools:

   • Resources:
     • Expert System Tools - Stanford University
   • Notes: List and describe popular languages and tools for expert systems.

4. Explanation Facilities and Knowledge Acquisition:

   • Resources:
     • Knowledge Acquisition - Coursera
   • Notes: Describe explanation facilities and methods for knowledge acquisition.

5. Applications:

   • Natural Language Processing:

     • Resources:
       • Sentiment Analysis - Towards Data Science
       • [Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper]
     • Notes: Framework for text processing, sentiment analysis case study.

   • Computer Vision:

     • Resources:
       • Object Recognition - Stanford University
       • [Deep Learning for Computer Vision by Rajalingappaa Shanmugamani]
     • Notes: Framework for CV applications, object recognition case study.

---

Link to original note: AI-Learning ResourcesAI-Learning ResourcesTo help you gain a deep understanding of Artificial Intelligence (AI), we'll create a structured learning path based on the provided syllabus. The plan includes a detailed breakdown of each unit, along with recommendations for resources such as research papers, blogs, articles, videos, and other multimedia content. Additionally, we'll suggest methods for annotating and making notes to optimize your learning experience. Unit I - Introduction to AI * Topics1 * Learning Path1 * Multimedia Content